A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control
---
paper_title: A logical calculus of the ideas immanent in nervous activity

paper_content:

Abstract Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed.
---
paper_title: Reinforcement Learning: An Introduction

paper_content:

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
---
paper_title: Lyapunov Functions Family Approach to Transient Stability Assessment

paper_content:

Analysis of transient stability of strongly nonlinear post-fault dynamics is one of the most computationally challenging parts of Dynamic Security Assessment. This paper proposes a novel approach for assessment of transient stability of the system. The approach generalizes the idea of energy methods, and extends the concept of energy function to a more general Lyapunov Functions Family (LFF) constructed via Semi-Definite-Programming techniques. Unlike the traditional energy function and its variations, the constructed Lyapunov functions are proven to be decreasing only in a finite neighborhood of the equilibrium point. However, we show that they can still certify stability of a broader set of initial conditions in comparison to the traditional energy function in the closest-UEP method. Moreover, the certificates of stability can be constructed via a sequence of convex optimization problems that are tractable even for large scale systems. We also propose specific algorithms for adaptation of the Lyapunov functions to specific initial conditions and demonstrate the effectiveness of the approach on a number of IEEE test cases.
---
paper_title: Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions

paper_content:

The problem of defining and classifying power system stability has been addressed by several previous CIGRE and IEEE Task Force reports. These earlier efforts, however, do not completely reflect current industry needs, experiences and understanding. In particular, the definitions are not precise and the classifications do not encompass all practical instability scenarios. This report developed by a Task Force, set up jointly by the CIGRE Study Committee 38 and the IEEE Power System Dynamic Performance Committee, addresses the issue of stability definition and classification in power systems from a fundamental viewpoint and closely examines the practical ramifications. The report aims to define power system stability more precisely, provide a systematic basis for its classification, and discuss linkages to related issues such as power system reliability and security.
---
paper_title: A BCU method for direct analysis of power system transient stability

paper_content:

A boundary of stability region based controlling unstable equilibrium point method (BCU method) for direct analysis of power system transient stability is presented. Features distinguishing the BCU method from the existing direct methods are that it consistently finds the exact controlling unstable equilibrium point (UEP) relative to a fault-on trajectory and that it has a sound theoretical basis. Moreover, the BCU method appears to be fast. This method is based on the relationship between the boundary of stability region of a power system and that of a reduced system. The BCU method finds the controlling UEP of the original system via a reduced system whose controlling UEP is easier and cheaper to compute. Effective numerical schemes to speed up the presented method are also proposed. This method has been tested on several power systems with very promising results. Simulation results on a 50 generator, 145 bus system are presented along with a comparison between the results obtained using the BCU method and another existing method. >
---
paper_title: Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions

paper_content:

The problem of defining and classifying power system stability has been addressed by several previous CIGRE and IEEE Task Force reports. These earlier efforts, however, do not completely reflect current industry needs, experiences and understanding. In particular, the definitions are not precise and the classifications do not encompass all practical instability scenarios. This report developed by a Task Force, set up jointly by the CIGRE Study Committee 38 and the IEEE Power System Dynamic Performance Committee, addresses the issue of stability definition and classification in power systems from a fundamental viewpoint and closely examines the practical ramifications. The report aims to define power system stability more precisely, provide a systematic basis for its classification, and discuss linkages to related issues such as power system reliability and security.
---
paper_title: Lyapunov Functions Family Approach to Transient Stability Assessment

paper_content:

Analysis of transient stability of strongly nonlinear post-fault dynamics is one of the most computationally challenging parts of Dynamic Security Assessment. This paper proposes a novel approach for assessment of transient stability of the system. The approach generalizes the idea of energy methods, and extends the concept of energy function to a more general Lyapunov Functions Family (LFF) constructed via Semi-Definite-Programming techniques. Unlike the traditional energy function and its variations, the constructed Lyapunov functions are proven to be decreasing only in a finite neighborhood of the equilibrium point. However, we show that they can still certify stability of a broader set of initial conditions in comparison to the traditional energy function in the closest-UEP method. Moreover, the certificates of stability can be constructed via a sequence of convex optimization problems that are tractable even for large scale systems. We also propose specific algorithms for adaptation of the Lyapunov functions to specific initial conditions and demonstrate the effectiveness of the approach on a number of IEEE test cases.
---
paper_title: Power grid transient stability prediction using wide area synchrophasor measurements

paper_content:

Electric power systems are prone to various kinds of transient disturbances which exist only for a fraction of second and often trigger cascading failures. Hence it is important to detect and prevent them from spreading in time. Conventionally these events are prevented by deploying costly special protection systems (SPS). Unfortunately, in many cases SPSs mis-operate as they could not predict the stability well ahead and are designed to operate based on past experiences and extensive off-line simulations. This paper proposes an online transient stability prediction scheme based on live synchrophasor data. The novelty of the proposed method is that it accurately predicts the transient stability based on only few (10 to 12) sample fault data without solving computationally extensive electromechanical dynamics. Synchrophasor data from geographically distributed Phasor Measurement Units (PMUs) are collected, synchronized, aggregated (if required) and analyzed on a stream computing platform to predict the trajectories of the generators which are then used to predict the transient stability of the grid. Performance of the proposed scheme is evaluated on the benchmark systems and evaluation results are presented in this paper.
---
paper_title: Application of Energy-Based Power System Features for Dynamic Security Assessment

paper_content:

This paper presents a novel approach to enable frequent computational cycles in online dynamic security assessment by using the terms of the transient energy function (TEF) as input features to a machine learning algorithm. The aim is to train a single classifier that is capable of classifying stable and unstable operating points independent of the contingency. The network is trained based on the current system topology and the loading conditions. The potential of the proposed approach is demonstrated with the New England 39-bus test power system model using the support vector machine as the machine learning technique. It is shown that the classifier can be trained using a small set of data when the terms of the TEF are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points.
---
paper_title: A BCU method for direct analysis of power system transient stability

paper_content:

A boundary of stability region based controlling unstable equilibrium point method (BCU method) for direct analysis of power system transient stability is presented. Features distinguishing the BCU method from the existing direct methods are that it consistently finds the exact controlling unstable equilibrium point (UEP) relative to a fault-on trajectory and that it has a sound theoretical basis. Moreover, the BCU method appears to be fast. This method is based on the relationship between the boundary of stability region of a power system and that of a reduced system. The BCU method finds the controlling UEP of the original system via a reduced system whose controlling UEP is easier and cheaper to compute. Effective numerical schemes to speed up the presented method are also proposed. This method has been tested on several power systems with very promising results. Simulation results on a 50 generator, 145 bus system are presented along with a comparison between the results obtained using the BCU method and another existing method. >
---
paper_title: WACS-Wide-Area Stability and Voltage Control System: R&D and Online Demonstration

paper_content:

As background, we describe frequently used feedforward wide-area discontinuous power system stability controls. Then we describe online demonstration of a new response-based (feedback) Wide-Area stability and voltage Control System (WACS). The control system uses powerful discontinuous actions for power system stabilization. The control system comprises phasor measurements at many substations, fiber-optic communications, real-time deterministic computers, and transfer trip output signals to circuit breakers at many other substations and power plants. Finally, we describe future development of WACS. WACS is developed as a flexible platform to prevent blackouts and facilitate electrical commerce.
---
paper_title: A comprehensive approach to transient stability control. I. Near optimal preventive control

paper_content:

A general approach to real-time transient stability control is proposed, and two complementary techniques are devised: one for preventive, the other for emergency control. In this paper, the general transient stability control approach is first revisited then applied to real-time preventive control. The technique consists of shifting active power generation. The amount of power and the machines from which to shift it are methodically determined, and various patterns of generation decrease/increase are considered. Further, a standard OPF algorithm is combined with this control technique to get a real-time transient stability-constrained OPF software, able to meet power system security and electricity market requirements. Simulations conducted on the 88-machine EPRI system and the Mexican interconnected power system illustrate the various techniques, highlight their specifics, and assess their performance.
---
paper_title: Sparsity-Promoting Optimal Wide-Area Control of Power Networks

paper_content:

Inter-area oscillations in bulk power systems are typically poorly controllable by means of local decentralized control. Recent research efforts have been aimed at developing wide- area control strategies that involve communication of remote signals. In conventional wide-area control, the control structure is fixed a priori typically based on modal criteria. In contrast, here we employ the recently-introduced paradigm of sparsity- promoting optimal control to simultaneously identify the optimal control structure and optimize the closed-loop performance. To induce a sparse control architecture, we regularize the standard quadratic performance index with an l1-penalty on the feedback matrix. The quadratic objective functions are inspired by the classic slow coherency theory and are aimed at imitating homogeneous networks without inter-area oscillations. We use the New England power grid model to demonstrate that the proposed combination of the sparsity-promoting control design with the slow coherency objectives performs almost as well as the optimal centralized control while only making use of a single wide-area communication link. In addition to this nominal performance, we also demonstrate that our control strategy yields favorable robustness margins and that it can be used to identify a sparse control architecture for control design via alternative means.
---
paper_title: Distributed Control Techniques in Microgrids

paper_content:

The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control.
---
paper_title: Sparsity-Promoting Optimal Wide-Area Control of Power Networks

paper_content:

Inter-area oscillations in bulk power systems are typically poorly controllable by means of local decentralized control. Recent research efforts have been aimed at developing wide- area control strategies that involve communication of remote signals. In conventional wide-area control, the control structure is fixed a priori typically based on modal criteria. In contrast, here we employ the recently-introduced paradigm of sparsity- promoting optimal control to simultaneously identify the optimal control structure and optimize the closed-loop performance. To induce a sparse control architecture, we regularize the standard quadratic performance index with an l1-penalty on the feedback matrix. The quadratic objective functions are inspired by the classic slow coherency theory and are aimed at imitating homogeneous networks without inter-area oscillations. We use the New England power grid model to demonstrate that the proposed combination of the sparsity-promoting control design with the slow coherency objectives performs almost as well as the optimal centralized control while only making use of a single wide-area communication link. In addition to this nominal performance, we also demonstrate that our control strategy yields favorable robustness margins and that it can be used to identify a sparse control architecture for control design via alternative means.
---
paper_title: Wide-Area Optimal Control of Electric Power Systems With Application to Transient Stability for Higher Order Contingencies

paper_content:

A real-time stabilizing control method for responding to N-K contingencies, with large K, is developed utilizing network and machine time-synchronized measurements. The controls follow an optimality principle in driving rotor-angles to an acceptable equilibrium point, at minimum cost, by predicting state response trajectory to a collection of stepped structural changes, from an admissible set, according to a defined model. A cost metric suitable for mitigating rotor-angle instability is developed. Non-idealities in modeling, measurement latency, control availability, and actuation success are investigated. It is shown how control over system structure in a feedback formulation increases the capability to handle higher order contingencies. As an experimental example, a set of simultaneous N-3 transient stability related contingencies are stabilized for the IEEE 39-bus system. Furthermore, the response after control actuation failure is investigated and it is shown that the system remains driven to a valid stable equilibrium point.
---
paper_title: Wide-area measurement based stabilizing control of large power systems-a decentralized/hierarchical approach

paper_content:

The aim of this paper is to assess the capability of the emerging synchronized phasor measurement technology in improving the overall stability of the Hydro-Quebec transmission system through supplementary modulation of voltage regulators. Following a thorough singular value and eigenvalue analysis of the system dynamic interactions, five control sites consisting of four generators and one synchronous condenser are chosen to implement new power system stabilizers with a supplementary input from remote phasor measuring units, geographically spread over nine electrically coherent areas. Since the remote feedback loops are built on top of an existing decentralized control system, this design approach results in a decentralized/hierarchical control architecture with significant advantages in terms of reliability and operational flexibility. A systematic control and measurement pairing yielded four dominant natural loops, each associated with a significant open-loop inter-area oscillatory mode at 0.06, 0.4, 0.7 and 0.95 Hz respectively. These PSSs have a speed sensitive local loop operating in the usual way, and a wide-area measurement based global loop which involves a single differential frequency signal between two suitably selected areas. The tuning and coordination technique for these advanced multiple input signals PSSs is described. Their impacts on the system is assessed using both small-signal analysis and nonlinear simulations in a transient stability program. Wide-area stabilizing controllers have a significant potential in improving the dynamic performance of the Hydro-Quebec's existing power system.
---
paper_title: Robust decentralized nonlinear controller design for multimachine power systems

paper_content:

In this paper, a robust decentralized excitation control scheme is proposed for multimachine power system transient stability enhancement. First, a direct feedback linearization (DFL) compensator through the excitation loop is designed to eliminate the nonlinearities and interconnections of the multimachine power system. Then, a robust decentralized controller is proposed to guarantee the asymptotic stability of the DFL compensated system considering the effects of plant parametric uncertainties and remaining nonlinear interconnections. The design procedure for an n-machine power system involves in solving n Riccati equations. In the design of the robust nonlinear decentralized controller, only the bounds of generator parameters need to be known, but not the transmission network parameters, system operating points or the fault locations. Since the proposed robust nonlinear decentralized controller can guarantee the stability of the large scale power system within the whole operating region for all admissible parameters, transient stability of the overall system can be greatly enhanced. The design procedure is tested on a three-machine example power system. Simulation results show that the proposed control scheme can greatly enhance the transient stability of the system regardless of the network parameters, operating points and fault locations.
---
paper_title: Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions

paper_content:

The problem of defining and classifying power system stability has been addressed by several previous CIGRE and IEEE Task Force reports. These earlier efforts, however, do not completely reflect current industry needs, experiences and understanding. In particular, the definitions are not precise and the classifications do not encompass all practical instability scenarios. This report developed by a Task Force, set up jointly by the CIGRE Study Committee 38 and the IEEE Power System Dynamic Performance Committee, addresses the issue of stability definition and classification in power systems from a fundamental viewpoint and closely examines the practical ramifications. The report aims to define power system stability more precisely, provide a systematic basis for its classification, and discuss linkages to related issues such as power system reliability and security.
---
paper_title: A real-time multiagent wide-area stabilizing control framework for power system transient stability enhancement

paper_content:

In this paper, the design of a network of real-time close-loop wide-area decentralized power system stabilizers (WD-PSSs) is investigated. In this approach, real-time wide-area measurement data are processed and utilized to design a set of stability agents based on the Reinforcement Learning (RL) method and the multiagent system theory. Recent technological advances in wide-area measurement system (WAMS) make the use of remote signals possible in designing power system controllers. The proposed stability agents are decentralized and autonomous, but they are able to cooperate to fulfill the design objectives. The main design objective is to damp power system oscillations quickly after severe disturbances. This paper describes the developed framework and different challenges in designing such a network and their solutions. Case studies using a four-machine power system model are provided to illustrate the performance of the proposed approach.
---
paper_title: Wide-Area Protection and its Applications - A Bibliographical Survey

paper_content:

Modern power systems are continuously developing to large and interconnected ones. The power industry restructuring and the reduced investment in transmission system expansion make power systems operate closer and closer to their limits, and hence lead to larger possibility of fault outages than before. Therefore, the protection and control in power systems become more and more important as well as complicated. On the other hand, the continuous technological development in communication and measurement accelerates the occurrence and applications of wide-area protection, a kind of advanced protections based on wide-area measurements. The blackouts happened in North America as well as other countries in the past few years also provide more and more incentive for scientists and engineers in the power system circle to devote to the study on wide-area protection and control systems. In this paper, a comprehensive bibliographical survey is made on recent development in this field, and the survey is done from seven relevant aspects
---
paper_title: Progress in supervised neural networks

paper_content:

Theoretical results concerning the capabilities and limitations of various neural network models are summarized, and some of their extensions are discussed. The network models considered are divided into two basic categories: static networks and dynamic networks. Unlike static networks, dynamic networks have memory. They fall into three groups: networks with feedforward dynamics, networks with output feedback, and networks with state feedback, which are emphasized in this work. Most of the networks discussed are trained using supervised learning. >
---
paper_title: A new hybrid critic-training method for approximate dynamic programming

paper_content:

A variety of methods for developing quasi-optimal intelligent control systems using reinforcement learning techniques based on adaptive critics have appeared in recent years. This paper reviews the family of approximate dynamic programming techniques based on adaptive critic methods and introduces a new hybrid critic training method.
---
paper_title: Power grid transient stability prediction using wide area synchrophasor measurements

paper_content:

Electric power systems are prone to various kinds of transient disturbances which exist only for a fraction of second and often trigger cascading failures. Hence it is important to detect and prevent them from spreading in time. Conventionally these events are prevented by deploying costly special protection systems (SPS). Unfortunately, in many cases SPSs mis-operate as they could not predict the stability well ahead and are designed to operate based on past experiences and extensive off-line simulations. This paper proposes an online transient stability prediction scheme based on live synchrophasor data. The novelty of the proposed method is that it accurately predicts the transient stability based on only few (10 to 12) sample fault data without solving computationally extensive electromechanical dynamics. Synchrophasor data from geographically distributed Phasor Measurement Units (PMUs) are collected, synchronized, aggregated (if required) and analyzed on a stream computing platform to predict the trajectories of the generators which are then used to predict the transient stability of the grid. Performance of the proposed scheme is evaluated on the benchmark systems and evaluation results are presented in this paper.
---
paper_title: Application of Energy-Based Power System Features for Dynamic Security Assessment

paper_content:

This paper presents a novel approach to enable frequent computational cycles in online dynamic security assessment by using the terms of the transient energy function (TEF) as input features to a machine learning algorithm. The aim is to train a single classifier that is capable of classifying stable and unstable operating points independent of the contingency. The network is trained based on the current system topology and the loading conditions. The potential of the proposed approach is demonstrated with the New England 39-bus test power system model using the support vector machine as the machine learning technique. It is shown that the classifier can be trained using a small set of data when the terms of the TEF are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points.
---
paper_title: 1 Reinforcement Learning and its Relationship to Supervised Learning

paper_content:

The modern study of approximate dynamic programming (DP) combines ideas from several research traditions. Among these is the field of Artificial Intelligence, whose earliest period focussed on creating artificial learning systems. Today, Machine Learning is an active branch of Artificial Intelligence (although it includes researchers from many other disciplines as well) devoted to continuing the development of artificial learning systems. Some of the problems studied in Machine Learning concern stochastic sequential decision processes, and some approaches to solving them are based on DP. These problems and algorithms fall under the general heading of reinforcement learning. In this chapter, we discuss stochastic sequential decision processes from the perspective of Machine Learning, focussing on reinforcement learning and its relationship to the more commmonly studied supervised learning problems. Machine Learning is the study ofmethods for constructing and improving software systems by analyzing examples of their behavior rather than by directly programming them. Machine Learning methods are appropriate in application settings where people are unable to provide precise specifications for desired program behavior, but where examples of desired behavior are available, or where it is possible to assign a measure of goodness to examples of behavior. Such situations include optical character recognition, handwriting recognition, speech recognition, automated steering of automobiles, and robot control and navigation. A key property of tasks in which examples of desired behavior are available is that people can perform them quite easily, but people cannot articulate exactly how they perform them. Hence, people
---
paper_title: Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques

paper_content:

A time-frequency-based approach for contingency severity ranking and rapid stability assessment is described. The aim is to classify correctly all single or multiple contingencies that may result in loss of voltage or frequency stability in the first 20 s following the last disturbing action. We start by selecting a number of strategic monitoring buses where the phasor measurement units are located to capture representative voltage magnitudes and angles during detailed time-domain simulations, which cover special protection systems and on-load tap changers. The short-time Fourier transform is then dynamically applied to the responses for extracting selected decision features as the simulation time evolves. It is shown that frequency-domain features such as the peak spectral density of the angle, frequency, and their dot product evaluated over the grid areas are reliable time-varying stability indicators that can form the basis of an entirely secure classification system able to respond within 2 to 3 s after the last event in the contingency. This allows early termination of about 60% of permanently stable simulations. Fuzzy logic and neural networks are used together to make initial decisions, which are then mixed by voting in order to improve the assessment reliability and security at the expense of a reduced yield. The proposed DSA scheme is successfully tested with 1027 contingencies from two widely differing test systems: a 67-bus fictitious system and a 783-bus system in actual use at Hydro-Quebec's operations planning department.
---
paper_title: Reinforcement Learning: An Introduction

paper_content:

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
---
paper_title: Enhancing power system security control

paper_content:

This article presents a new concept for expanding the scope of power system security functions in an EMS control center, an approach which combines automatic learning applied to dynamic security analysis and a changeover of the conventional operator training simulator (OTS) into a dynamic OTS. Automatic learning is shown to be suitable for power system dynamic security analysis, provided training-set generation is effective and the output adds value to the decision-making process.
---
paper_title: Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof

paper_content:

Convergence of the value-iteration-based heuristic dynamic programming (HDP) algorithm is proven in the case of general nonlinear systems. That is, it is shown that HDP converges to the optimal control and the optimal value function that solves the Hamilton-Jacobi-Bellman equation appearing in infinite-horizon discrete-time (DT) nonlinear optimal control. It is assumed that, at each iteration, the value and action update equations can be exactly solved. The following two standard neural networks (NN) are used: a critic NN is used to approximate the value function, whereas an action network is used to approximate the optimal control policy. It is stressed that this approach allows the implementation of HDP without knowing the internal dynamics of the system. The exact solution assumption holds for some classes of nonlinear systems and, specifically, in the specific case of the DT linear quadratic regulator (LQR), where the action is linear and the value quadratic in the states and NNs have zero approximation error. It is stressed that, for the LQR, HDP may be implemented without knowing the system A matrix by using two NNs. This fact is not generally appreciated in the folklore of HDP for the DT LQR, where only one critic NN is generally used.
---
paper_title: Global optimality of approximate dynamic programming and its use in non-convex function minimization

paper_content:

Level curves of the Rosenbrock function subject to minimization and state trajectories for different initial conditions x0ź{-2, -1, 0, 1, 2}×{-2, -1, 0, 1, 2}. The red plus signs denote the initial point of the respective trajectory. This study investigates the global optimality of approximate dynamic programming (ADP) based solutions using neural networks for optimal control problems with fixed final time. Issues including whether or not the cost function terms and the system dynamics need to be convex functions with respect to their respective inputs are discussed and sufficient conditions for global optimality of the result are derived. Next, a new idea is presented to use ADP with neural networks for optimization of non-convex smooth functions. It is shown that any initial guess leads to direct movement toward the proximity of the global optimum of the function. This behavior is in contrast with gradient based optimization methods in which the movement is guided by the shape of the local level curves. Illustrative examples are provided with single and multi-variable functions that demonstrate the potential of the proposed method.
---
paper_title: A Particle Swarm Optimization Method for Power System Dynamic Security Control

paper_content:

This paper proposes an automatic learning framework for the dynamic security control of a power system. The proposed method employs a radial basis function neural network (RBFNN), which serves to assess the dynamic security status of the power system and to estimate the effect of a corrective control action applied in the event of a disturbance. Particle swarm optimization is applied to find the optimal control action, where the objective function to be optimized is provided by the RBFNN. The method is applied on a realistic model of the Hellenic Power System and on the IEEE 50-generator test system, and its added value is shown by comparing results with the ones obtained from the application of other machine learning methods.
---
paper_title: Radial basis function (RBF) network adaptive power system stabilizer

paper_content:

Summary form only given as follows. This paper presents a new approach for real-time tuning the parameters of a conventional power system stabilizer (PSS) using a radial basis function (RBF) network. The RBF network is trained using an orthogonal least squares (OLS) learning algorithm. Investigations reveal that the required number of RBF centers depends on spread factor, /spl beta/ and the number of training patterns. Studies show that a parsimonious RBF network can be obtained by presenting a relatively smaller number of training patterns, generated randomly and spreadover the entire operating domain. Investigations reveal that the dynamic performance of the system with an RBF network adaptive PSS (RBFAPSS) is virtually identical to that of an artificial neural network based adaptive PSS (ANNAPSS). The dynamic performance of the system with RBFAPSS is quite robust over a wide range of loading conditions and equivalent reactance X/sub c/.
---
paper_title: Identification and control of dynamical systems using neural networks

paper_content:

It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described. >
---
paper_title: Comparison of Adaptive Critic-Based and Classical Wide-Area Controllers for Power Systems

paper_content:

An adaptive critic design (ACD)-based damping controller is developed for a thyristor-controlled series capacitor (TCSC) installed in a power system with multiple poorly damped interarea modes. The performance of this ACD computational intelligence-based method is compared with two classical techniques, which are observer-based state-feedback (SF) control and linear matrix inequality LMI-Hinfin robust control. Remote measurements are used as feedback signals to the wide-area damping controller for modulating the compensation of the TCSC. The classical methods use a linearized model of the system whereas the ACD method is purely measurement-based, leading to a nonlinear controller with fixed parameters. A comparative analysis of the controllers' performances is carried out under different disturbance scenarios. The ACD-based design has shown promising performance with very little knowledge of the system compared to classical model-based controllers. This paper also discusses the advantages and disadvantages of ACDs, SF, and LMI-Hinfin.
---
paper_title: Virtual generators: Simplified online power system representations for wide-area damping control

paper_content:

This paper introduces a new concept called a Virtual Generator (VG). VGs are simplified representations of groups of coherent synchronous generators in a power system. They resemble commonly used power system dynamic equivalents obtained via generator aggregation techniques. Traditionally power system dynamic equivalents are developed offline, fixed, and used to replace large portions of the system that are considered external to the portion of the system being analyzed in detail. In contrast, VGs are calculated online, are not limited to representing external areas of the system being analyzed/controlled, and do not replace any portion of the power system. Instead, they allow wide-area damping controllers (WADCs) to exploit the realization that a group of coherent synchronous generators in a power system can be controlled as a single generating unit for achieving wide-area damping control objectives. The implementation of VGs is made possible by the availability of Wide-Area Measurements (WAMs) from Phasor Measurement Units (PMUs). To the authors' knowledge, this is the first time that the use of power system equivalencing techniques has been extended to real-time WADC. Simulation studies carried out on the 68-bus New England/New York power system demonstrate that intelligent controllers developed using VGs can significantly improve the stability of a power system by effectively damping low-frequency interarea oscillations.
---
paper_title: A real-time wide-area excitation control to enhance transient and oscillatory stabilities

paper_content:

A real-time control scheme based on wide-area measurements and excitation control of generators is introduced in this paper. The wide-area measurements are preprocessed in a central unit. The processed data are then used by stability agents which are decentralized and autonomous. The goals of these controllers are to stabilize the system after severe disturbances and mitigate the oscillations afterward. A reinforcement learning algorithm is used to design the controllers. The proposed method extends the stability boundary of the system and achieves the above goals without losing any generator or load area and without any knowledge of the disturbances causing the response. The effectiveness of the proposed control scheme is illustrated through case studies on the IEEE 39-bus power system.
---
paper_title: Communication network modeling and simulation for Wide Area Measurement applications

paper_content:

In the recent years, Phasor Measurement Unit (PMU) based Wide Area Measurement System (WAMS) has been receiving ever increasing attention from the academia as well as from the industry. Power utilities have been designing and implementing WAMS to provide more intelligent monitoring, control, and protection of the power grid. In order to achieve real-time operations in the modern power systems, construction of an economic and efficient communication infrastructure is a necessity, and various utilities have been laying fiber optical network along their transmission and distribution right of way to leverage the abilities of PMUs in providing greater visibility over a larger area of the grid - thereby providing opportunities for better control and stability. The choice of network architecture, protocols, and various measures for quality of service guarantees must be made by the network architects at the utilities. In this paper, we present a methodology based on profiling data traffic for various WAMS applications according to their communication requirements, and then creating simulation models and scenarios to obtain various parameters for specific architectural and protocol choices. Our simulation results are encouraging in the sense that under modest choices all the applications meet the timing and bandwidth requirements. However, the main contribution of this work is the methodology that would allow the utilities to evaluate various communication infrastructure choices while deploying WAMS.
---
paper_title: Optimal Wide Area Controller and State Predictor for a Power System

paper_content:

An optimal wide area controller is designed in this paper for a 12-bus power system together with a Static Compensator (STATCOM). The controller provides auxiliary reference signals for the automatic voltage regulators (AVR) of the generators as well as the line voltage controller of the STATCOM in such a way that it improves the damping of the rotor speed deviations of the synchronous machines. Adaptive critic designs theory is used to implement the controller and enable it to provide nonlinear optimal control over the infinite horizon time of the problem and at different operating conditions of the power system. Simulation results are provided to indicate that the proposed wide area controller improves the damping of the rotor speed deviations of the generators during large scale disturbances. Moreover, a robust radial basis function network based identifier is presented in this paper to predict the states of a multimachine power system in real-time. This wide area state predictor (WASP) compensates for transport lags associated with the present communication technology for wide area monitoring of the electric power grid. The WASP is also robust to partial loss of information caused by larger than expected transport lags or even failed sensors throughout the network
---
paper_title: Parameter-free fault location for transmission lines based on optimisation

paper_content:

An accurate algorithm for locating faults on two-terminal transmission lines is presented in this study. The presented method does not require values of line parameters to locate the fault; therefore variation of line parameter values does not affect its accuracy. The method utilises synchronous post-fault measurements of currents and voltages in both terminals and is based on the distributed time-domain representation of transmission lines. Unlike conventional methods, fault location is converted to an optimisation problem. Then, this problem is solved by genetic algorithm to find the accurate location of fault. Furthermore, modifying the problem to be solved by the Newton optimisation method is discussed in this study. Simulation results performed by ATP/EMTP verify high accuracy of the presented method, and reveal that it is not significantly affected by fault resistance, fault type, fault inception angle and source impedance. Moreover, effect of measurement errors and synchronisation errors on the accuracy of presented method have been analysed which verify the efficiency of the algorithm in these conditions.
---
paper_title: Wide-Area Monitoring, Protection, and Control of Future Electric Power Networks

paper_content:

Wide-area monitoring, protection, and control (WAMPAC) involves the use of system-wide information and the communication of selected local information to a remote location to counteract the propagation of large disturbances. Synchronized measurement technology (SMT) is an important element and enabler of WAMPAC. It is expected that WAMPAC systems will in the future reduce the number of catastrophic blackouts and generally improve the reliability and security of energy production, transmission, and distribution, particularly in power networks with a high level of operational uncertainties. In this paper, the technological and application issues are addressed. Several key monitoring, protection, and control applications are described and discussed. A strategy for developing a WAMPAC system in the United Kingdom is given as well.
---
paper_title: Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges

paper_content:

This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.
---
paper_title: Generator excitation systems sensitivity analysis and their model parameter's reduction

paper_content:

Since power systems have large scale, nonlinear parameters, including uncertainty, researchers have presented many methods for model simplification. Voltage dip and transients are known for their importance in dynamic studies and as a result of short circuits or starting a large induction motor, generator excitation systems come into effect in order to compensate voltage oscillations and voltage dips during such transients. This paper will address model parameter reduction of generator excitation systems. An objective function and a simple method for sensitivity analysis will be proposed to identify which excitation parameters are more sensitive and have more impact on generator voltage response. Moreover, the proposed method can be used to determine which parameters do not have significant effect on the excitation system. The objective function will stress voltage dip and overshoot rather than small oscillations and transients. A small stand-alone micro gird, with three induction motors and a generator with IEEE ST2A excitation system have been selected to simulate and verify the method performance.
---
paper_title: A real-time wide-area excitation control to enhance transient and oscillatory stabilities

paper_content:

A real-time control scheme based on wide-area measurements and excitation control of generators is introduced in this paper. The wide-area measurements are preprocessed in a central unit. The processed data are then used by stability agents which are decentralized and autonomous. The goals of these controllers are to stabilize the system after severe disturbances and mitigate the oscillations afterward. A reinforcement learning algorithm is used to design the controllers. The proposed method extends the stability boundary of the system and achieves the above goals without losing any generator or load area and without any knowledge of the disturbances causing the response. The effectiveness of the proposed control scheme is illustrated through case studies on the IEEE 39-bus power system.
---
paper_title: A Computational Approach to Optimal Damping Controller Design for a GCSC

paper_content:

This paper presents a computational approach for an offline measurement-based design of an optimal damping controller using adaptive critics for a new type of flexible ac transmission system device-the gate-controlled series capacitor (GCSC). Remote measurements are provided to the controller to damp out system modes. The optimal controller is developed based on the heuristic dynamic programming (HDP) approach. Three multilayer-perceptron neural networks are used in the design-the identifier/model network to identify the dynamics of the power system, the critic network to evaluate the performance of the damping controller, and the controller network to provide optimal damping. This measurement-based technique provides an alternative to the classical linear model-based optimal control design. The eigenvalue analysis of the closed-loop system is performed with time-domain responses using the Prony method. An analysis of the simulation results shows potential of the HDP-based optimal damping controller on a GCSC for enhancing the stability of the power system.
---
paper_title: Optimal Wide Area Controller and State Predictor for a Power System

paper_content:

An optimal wide area controller is designed in this paper for a 12-bus power system together with a Static Compensator (STATCOM). The controller provides auxiliary reference signals for the automatic voltage regulators (AVR) of the generators as well as the line voltage controller of the STATCOM in such a way that it improves the damping of the rotor speed deviations of the synchronous machines. Adaptive critic designs theory is used to implement the controller and enable it to provide nonlinear optimal control over the infinite horizon time of the problem and at different operating conditions of the power system. Simulation results are provided to indicate that the proposed wide area controller improves the damping of the rotor speed deviations of the generators during large scale disturbances. Moreover, a robust radial basis function network based identifier is presented in this paper to predict the states of a multimachine power system in real-time. This wide area state predictor (WASP) compensates for transport lags associated with the present communication technology for wide area monitoring of the electric power grid. The WASP is also robust to partial loss of information caused by larger than expected transport lags or even failed sensors throughout the network
---
paper_title: Enhanced wide area monitoring system

paper_content:

This paper focuses on the design of a wide area monitoring system (WAMS) in a multimachine power system with enhanced reliability, integrity and security. A two-area four-machine power system is considered for the studies. The voltage and speed deviation information coming from the generators is used by the WAM to feed useful information to the wide area control system (WACS), which will then send control signals back to the power system. The proposed WAM uses current and past information from the generators to predict their future values. By combining features such as missing sensor fault tolerance, intrusion detection system and integrity check at the receiving end, the proposed method is more reliable and secured. The proposed EWAMS is capable of overcoming communication delays, mitigating attacks and surviving faults. Results for some modules of the system are shown in support of the proposition.
---
paper_title: Virtual generators: Simplified online power system representations for wide-area damping control

paper_content:

This paper introduces a new concept called a Virtual Generator (VG). VGs are simplified representations of groups of coherent synchronous generators in a power system. They resemble commonly used power system dynamic equivalents obtained via generator aggregation techniques. Traditionally power system dynamic equivalents are developed offline, fixed, and used to replace large portions of the system that are considered external to the portion of the system being analyzed in detail. In contrast, VGs are calculated online, are not limited to representing external areas of the system being analyzed/controlled, and do not replace any portion of the power system. Instead, they allow wide-area damping controllers (WADCs) to exploit the realization that a group of coherent synchronous generators in a power system can be controlled as a single generating unit for achieving wide-area damping control objectives. The implementation of VGs is made possible by the availability of Wide-Area Measurements (WAMs) from Phasor Measurement Units (PMUs). To the authors' knowledge, this is the first time that the use of power system equivalencing techniques has been extended to real-time WADC. Simulation studies carried out on the 68-bus New England/New York power system demonstrate that intelligent controllers developed using VGs can significantly improve the stability of a power system by effectively damping low-frequency interarea oscillations.
---
paper_title: Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning

paper_content:

In this paper, a novel framework for designing and implementing a coordinated wide-area controller architecture for improved power system dynamic stability is presented and tested. The algorithm is an optimal wide-area system-centric controller and observer based on a hybrid reinforcement learning and temporal difference framework. It allows the system to deal with major concerns of wide-area monitoring problem: delays in signal transmission, the uncertainty of the communication network, and data traffic. The main advantage of this design is its ability to learn from the past using eligibility traces and predict the optimal trajectory of cost function through temporal difference method. The control algorithm is evolved from adaptive critic design (ACD) and performed online at a finite horizon through backward and forward view. The ACD controller's training and testing are implemented on the Innovative Integration Picolo card integrated to TMS320C28335 processor. Results on a real experimental test bed using a real power system feeder shows that this architecture provides better stability compared with conventional schemes.
---
paper_title: Optimal Wide Area Controller and State Predictor for a Power System

paper_content:

An optimal wide area controller is designed in this paper for a 12-bus power system together with a Static Compensator (STATCOM). The controller provides auxiliary reference signals for the automatic voltage regulators (AVR) of the generators as well as the line voltage controller of the STATCOM in such a way that it improves the damping of the rotor speed deviations of the synchronous machines. Adaptive critic designs theory is used to implement the controller and enable it to provide nonlinear optimal control over the infinite horizon time of the problem and at different operating conditions of the power system. Simulation results are provided to indicate that the proposed wide area controller improves the damping of the rotor speed deviations of the generators during large scale disturbances. Moreover, a robust radial basis function network based identifier is presented in this paper to predict the states of a multimachine power system in real-time. This wide area state predictor (WASP) compensates for transport lags associated with the present communication technology for wide area monitoring of the electric power grid. The WASP is also robust to partial loss of information caused by larger than expected transport lags or even failed sensors throughout the network
---
paper_title: Latency Viewed as a Stochastic Process and its Impact on Wide Area Power System Control Signals

paper_content:

A method of calculating the communication delay (latency) for measurements and control signals in a power system is shown. The basis of the calculation is a dedicated communication channel for control signals. The time delay calculation is examined using a dynamic equivalent of the Western Electricity Coordinating Council transmission system. The impact on control system response is discussed. The application given is a wide area control system for interarea mode damping. Results demonstrate that control signal latency can degrade the performance of controls in a wide area control system.
---
paper_title: Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning

paper_content:

In this paper, a novel framework for designing and implementing a coordinated wide-area controller architecture for improved power system dynamic stability is presented and tested. The algorithm is an optimal wide-area system-centric controller and observer based on a hybrid reinforcement learning and temporal difference framework. It allows the system to deal with major concerns of wide-area monitoring problem: delays in signal transmission, the uncertainty of the communication network, and data traffic. The main advantage of this design is its ability to learn from the past using eligibility traces and predict the optimal trajectory of cost function through temporal difference method. The control algorithm is evolved from adaptive critic design (ACD) and performed online at a finite horizon through backward and forward view. The ACD controller's training and testing are implemented on the Innovative Integration Picolo card integrated to TMS320C28335 processor. Results on a real experimental test bed using a real power system feeder shows that this architecture provides better stability compared with conventional schemes.
---
paper_title: Reinforcement Learning: An Introduction

paper_content:

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
---
paper_title: Adaptive critic designs

paper_content:

We discuss a variety of adaptive critic designs (ACDs) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: heuristic dynamic programming, dual heuristic programming, and globalized dual heuristic programming (GDHP). The main emphasis is on DHP and GDHP as advanced ACDs. We suggest two new modifications of the original GDHP design that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACDs. This leads to a generalized training procedure for ACDs.
---
paper_title: Energy Function Inspired Value Priority Based Global Wide-Area Control of Power Grid

paper_content:

Conventional wide-area control strategies that are deployed to damp inter-area oscillations use fixed a priori schemes based on modal analysis of the power system. As these controllers cannot be tuned online, such schemes fail to perform well during severe dynamic system changes. In this paper, a wide-area control architecture designed based on reinforcement learning and optimal adaptive critic network is proposed, that learns and optimizes the system closed-loop performance. Also, a value priority scheme is designed using a derived Lyapunov energy function for prioritization of local and the proposed wide-area global controller which ensures coherent damping of local and inter-area oscillations. The method increases the reliability and allows for automatic tuning of stabilizing controllers especially in the presence of wide-area monitoring constraints. Simulation results on 8-bus 5-machine and 68-bus 16-machine IEEE test systems highlight the efficiency of the proposed method.
---
paper_title: Optimal Wide Area Controller and State Predictor for a Power System

paper_content:

An optimal wide area controller is designed in this paper for a 12-bus power system together with a Static Compensator (STATCOM). The controller provides auxiliary reference signals for the automatic voltage regulators (AVR) of the generators as well as the line voltage controller of the STATCOM in such a way that it improves the damping of the rotor speed deviations of the synchronous machines. Adaptive critic designs theory is used to implement the controller and enable it to provide nonlinear optimal control over the infinite horizon time of the problem and at different operating conditions of the power system. Simulation results are provided to indicate that the proposed wide area controller improves the damping of the rotor speed deviations of the generators during large scale disturbances. Moreover, a robust radial basis function network based identifier is presented in this paper to predict the states of a multimachine power system in real-time. This wide area state predictor (WASP) compensates for transport lags associated with the present communication technology for wide area monitoring of the electric power grid. The WASP is also robust to partial loss of information caused by larger than expected transport lags or even failed sensors throughout the network
---
paper_title: Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning

paper_content:

In this paper, a novel framework for designing and implementing a coordinated wide-area controller architecture for improved power system dynamic stability is presented and tested. The algorithm is an optimal wide-area system-centric controller and observer based on a hybrid reinforcement learning and temporal difference framework. It allows the system to deal with major concerns of wide-area monitoring problem: delays in signal transmission, the uncertainty of the communication network, and data traffic. The main advantage of this design is its ability to learn from the past using eligibility traces and predict the optimal trajectory of cost function through temporal difference method. The control algorithm is evolved from adaptive critic design (ACD) and performed online at a finite horizon through backward and forward view. The ACD controller's training and testing are implemented on the Innovative Integration Picolo card integrated to TMS320C28335 processor. Results on a real experimental test bed using a real power system feeder shows that this architecture provides better stability compared with conventional schemes.
---
paper_title: Hybrid energy function based real-time optimal wide-area transient stability controller for power system stability

paper_content:

This paper presents a hybrid direct and intelligent method of real-time coordinated wide-area controller for improved power system transient stability. The algorithm is applied as an optimal Wide-Area System-Centric Controller and Observer (WASCCO) based on Adaptive Critic Design (ACD). ACD techniques that uses Reinforcement Learning (RL) could be utilized to approximate the transient energy function by dynamic programming and find the solution to nonlinear optimal control problem. However, such technique is highly dependent on the cost function and its dynamics. A Lyupanov-based energy function that is defined offline and updated in real-time through prony analysis is utilized for this purpose. Results on a two area power system and 68-bus New England New York system shows better response compared to conventional schemes and local power system stabilizers.
---
paper_title: Reinforcement learning approach for congestion management and cascading failure prevention with experimental application

paper_content:

Abstract This article proposes a method based on the reinforcement learning (RL) for preventing cascading failure (CF) and blackout in smart grids by acting on the output power of the generators in real-time. The proposed research work utilizes the Q-learning algorithm to train the system for the optimal action selection strategy during the state-action learning process by updating the action values based on the obtained rewards. The trained system then is able to relieve congestion of transmission lines in real-time by adjusting the output power of the generators (actions) to prevent consecutive line outages and blackout after N-1 and N-1-1 contingency conditions. The proposed RL-based control is validated through experimental implementation as well as simulation studies on the IEEE 118-bus test system for different contingency case studies. The results obtained from the experimental and simulation studies show the accuracy and robustness of the proposed approach in preventing cascading failure and blackout.
---
paper_title: A Novel System-Centric Intelligent Adaptive Control Architecture for Power System Stabilizer Based on Adaptive Neural Networks

paper_content:

In this paper, an intelligent power system stabilizer based on a novel system-centric controller is proposed. The proposed method uses hybrid architecture with two algorithms: one, a neural network-based controller with explicit neuro-identifier, and the other, an adaptive controller implemented as a model reference adaptive controller (MRAC). The neuro-controller-identifier combination is used to approximate the nonlinear functional dynamics of the power system, and the MRAC controller adapts when power system (plant) parametric set changes. Additionally, a feed forward neural network (FFNN) identifier is used to predict system responses, and the control signals are adjusted in real-time to obtain improved system response. The FFNN is trained offline with extensive test data and is also adjusted online. The main advantage and uniqueness of the proposed scheme is the controllers' ability to complement each other in case of parametric and functional uncertainty and evolve in the presence of changing system dynamics. The theoretical results are validated by conducting simulation studies for electric-generator stabilization on a single-machine infinite-bus system and a two-area equivalent five-generator eight-bus multimachine power system with varying generator schedules that show fundamental subsynchronous oscillations.
---
paper_title: Adaptive Mixtures of Local Experts

paper_content:

We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.
---
paper_title: Value priority based optimal power system stabilization of generating resources using local and Global Controllers

paper_content:

In this paper, an intelligent supervisory level power system stabilizer based on value based prioritization of Reinforcement Learning (RL) and Supervised Learning (SL) is proposed. The proposed method uses a composite architecture integrating two controllers: First, an Adaptive Critic Design (ACD) implemented on neural network benchmark capable of approximating the nonlinear functional dynamics of the power system and second, a conventional Power System Stabilizer (PSS) as a local controller. The value priority is defined using a Lyuponov function candidate derived based on system stability analysis to indicate identification and performance quality of the controllers. This method increases the reliability and allows for automatic tuning of stabilizing controllers. The theoretical results are validated by conducting simulation studies for electric-generator stabilization on 39-bus 10-generator IEEE power system.
---
paper_title: Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

paper_content:

The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.
---
paper_title: The impact of large scale wind power generation on power system oscillations

paper_content:

Abstract Increasing amounts of wind turbines are connected to electrical power systems. This affects many aspects of their operation and behaviour. In this paper, the impact of large scale wind power generation on power system oscillations is treated. The three main types of power system oscillations, namely oscillations of a group of generators against a strong system and intra- and inter-area oscillations are studied. To this end, test systems are used. The effect of wind power on the oscillations is investigated by gradually replacing the power generated by the synchronous generators in the system by power from either constant or variable speed wind turbines, while observing the movement of the eigenvalues through the complex plane. From the results, it is concluded that the effect of wind power on power system oscillations depends on the wind turbine concept (constant or variable speed) and on the wind power penetration. In most cases, an increase in frequency and damping of power system oscillations was observed. This observation is caused by a relative reduction in size of the synchronous generators that engage in power system oscillations, which is equivalent to strengthening their mutual coupling. Further, it was shown that constant speed wind turbines damp power system oscillations more than variable speed turbines. This is caused by the damping effect of the squirrel cage induction generator used in constant speed wind turbines.
---
paper_title: Transient Stability of DFIG Wind Turbines at an External Short-circuit Fault

paper_content:

The fast development of wind power generation brings new requirements for wind turbine integration into the network. After clearance of an external short-circuit fault, grid-connected wind turbines should restore their normal operation without power loss caused by disconnections. This article concentrates on the transient stability of variable speed wind turbines with doubly fed induction generators (DFIGs) at an external short-circuit fault. A simulation model of a MW-level variable speed wind turbine with a DFIG developed in PSCAD/EMTDC is presented and the control and protection schemes are described in detail. The transient process of grid-connected wind turbines with DFIGs at an external short-circuit fault is analysed, and in critical post-fault situations a measure is proposed for the voltage recovery of DFIG wind turbines after fault clearance. Simulation results demonstrate that in uncritical post-fault situations the control schemes are able to restore the wind turbine's normal operation without disconnections. It is also proved that the proposed measure is effective in re-establishing the voltage at the wind turbine terminal in critical post-fault situations. Copyright © 2005 John Wiley & Sons, Ltd.
---
paper_title: Rotor Angle Stability With High Penetrations of Wind Generation

paper_content:

This paper explores the relationship between wind generation, particularly the control of reactive power from variable speed wind turbine generators, and the rotor angle stability of the conventional synchronous generators in the system. Rotor angle stability is a dynamic phenomenon generally associated with changes in active power flows that create angular separation between synchronous units in the system. With larger penetrations of wind generation being introduced into power systems, there will be large flows of active power from asynchronous generation in the system. These asynchronous active power flows can aid in maintaining the rotor angle stability of the system. However, the manner in which wind generation injects reactive power into the system can be critical in maintaining angular stability of the synchronous units. Utilizing wind generation to control voltage and reactive power in the system can ease the reactive power burden on synchronous generators, and minimize angular separation in the system following a contingency event and can provide a significant level of support which will become increasingly important in future power systems.
---
paper_title: Impact of increased penetration of DFIG based wind turbine generators on transient and small signal stability of power systems

paper_content:

The targeted and current development of wind energy in various countries around the world reveals that wind power is the fastest growing power generation technology. Among the several wind generation technologies, variable speed wind turbines utilizing doubly fed induction generators (DFIGs) are gaining momentum in the power industry. With the increase in penetration of these wind turbines, the power system dominated by synchronous machines will experience a change in dynamics and operational characteristics. Given this assertion, the present paper develops an approach to analyze the impact of increased penetration of DFIG-based wind turbines on transient and small signal stability of a large power system. The primary basis of the method is to convert the DFIG machines into equivalent conventional round rotor synchronous machines and then evaluate the sensitivity of the eigenvalues with respect to inertia. In this regard, modes that are both detrimentally and beneficially affected by the change in inertia are identified. These modes are then excited by appropriate disturbances and the impact of reduced inertia on transient stability performance is also examined. The proposed technique is tested on a large test system representing the Midwestern portion of the U.S. interconnection. The results obtained indicate that the proposed method effectively identifies both detrimental and beneficial impacts of increased DFIG penetration both for transient stability and small signal stability related performance.
---
paper_title: Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions

paper_content:

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using backpropagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.
---
paper_title: Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

paper_content:

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability.
---
paper_title: Nonlinear control of the doubly-fed induction generator in wind power systems

paper_content:

This paper describes the models of a wind power system, such as the turbine, generator, power electronics converters and controllers, with the aim to control the generation of wind power in order to maximize the generated power with the lowest possible impact in the grid voltage and frequency during normal operation and under the occurrence of faults. The presented work considers a wind power system equipped with the doubly-fed induction generator and a vector-controlled converter connected between the rotor and the grid. The paper presents comparative results between proportional-integral controllers and neural networks based controllers, showing that better dynamic characteristics can be obtained using neural networks based controllers.
---
paper_title: Design and Implementation of Neuro-Fuzzy Vector Control for Wind-Driven Doubly-Fed Induction Generator

paper_content:

Wound-rotor induction generators have numerous advantages in wind power generation over other types of generators. One scheme is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power. This configuration is called the doubly-fed induction generator (DFIG). In this paper, a vector control scheme is developed to control the rotor side voltage source converter that allows independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point. A neuro-fuzzy gain tuner is proposed to control the DFIG. The input for each neuro-fuzzy system is the error value of generator speed, active or reactive power. The choice of only one input to the system simplifies the design. Experimental investigations have also been conducted on a laboratory DFIG to verify the calculated results.
---
paper_title: Artificial neural networks in renewable energy systems applications:a review

paper_content:

Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling such as in implementing complex mappings and system identification. This paper presents various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic trough collector intercept factor and local concentration ratio and for the modelling and performance prediction of solar water heating systems. They have also been used for the estimation of heating loads of buildings, for the prediction of air flow in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all those models a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use. The work of other researchers in the field of renewable energy and other energy systems is also reported. This includes the use of artificial neural networks in solar radiation and wind speed prediction, photovoltaic systems, building services systems and load forecasting and prediction.
---
paper_title: Direct intelligent wide-area damping controller for wind integrated power system

paper_content:

In this paper an intelligent wide-area damping controller is proposed for a wind integrated power grid. The proposed design is based on an energy function based intelligent optimal controller evolved from adaptive critic design using reinforcement learning technique. A Lyapunov function candidate is derived that links the energy function and the transient stability of the integrated system with the wide-area controller. The proposed wide-area controller augments the excitation system of the synchronous generator and local Maximum Power Point (MPP) tracking control of wind generator Doubly Fed Induction Generator (DFIG). The theoretical results are validated by conducting simulation studies on 2-Area Kundur power system for damping the inter-area oscillations. It was observed that the method improves transient stability of the integrated system due to the active and reactive support from the DFIG and thus damping the inter-area oscillations much faster. It was also observed that the speed oscillations of DFIG is within the limits.
---
paper_title: Adaptive Critic Design-Based Dynamic Stochastic Optimal Control Design for a Microgrid With Multiple Renewable Resources

paper_content:

This paper proposes a three-layer optimization and an intelligent control algorithm for a microgrid with multiple renewable resources. A dual heuristic dynamic programming-based system control layer is used to ensure the dynamic performance and voltage dynamics of the microgrid as the system operation conditions change. A local layer maximizes the capability of the photovoltaic (PV) wind power generators and battery systems, and a model predictive control-based device layer increases the tracking accuracy of the converter control. The proposed control scheme, system wide adaptive predictive supervisory control (SWAPSC) smooths the output of PV and wind generators under intermittencies, maintains bus voltage by providing dynamic reactive power support to the grid, and reduces the total system losses while minimizing degradation of battery life span. Performance comparisons are made with and without SWAPSC for an IEEE 13 node test system with a PV farm, a wind farm, and two battery-based energy storage systems.
---
paper_title: DHP-Based Wide-Area Coordinating Control of a Power System with a Large Wind Farm and Multiple FACTS Devices

paper_content:

Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area monitor and wide-area coordinating neurocontroller (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm, and multiple flexible ac transmission system (FACTS) devices. The wide-area monitor is a radial basis function neural network (RBFNN) that identifies the input-output dynamics of the nonlinear power system. Its parameters are optimized through a particle swarm optimization (PSO) based method. The WACNC is designed by using the dual heuristic programming (DHP) method and RBFNNs. It operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance, and therefore helps improve system-wide dynamic and transient performance.
---

Title: A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control

Section 1: INTRODUCTION
Description 1: This section introduces the power system stability problem, discusses the complexities and challenges, and presents the role of Neural Networks (NNs) in addressing these issues.

Section 2: ROTOR-ANGLE STABILITY
Description 2: This section explores the concept of rotor angle stability, the dynamics of synchronous generators, and the classification into small signal stability and transient stability.

Section 3: Rotor Angle Stability Assessment
Description 3: This section provides an overview of small signal stability assessment (SSAT) and transient stability assessment (TSA), discussing methodologies and conventional approaches.

Section 4: POWER SYSTEM CONTROL HIERARCHY
Description 4: This section details the hierarchy of power system control, including local controllers, secondary and tertiary controllers, and Wide-Area Control (WAC).

Section 5: MACHINE LEARNING ALGORITHMS
Description 5: This section explains supervised learning and reinforcement learning algorithms, focusing on their methodologies, advantages, and applications in power system control.

Section 6: NN-BASED TRANSIENT STABILITY ASSESSMENT
Description 6: This section covers the application of neural networks and other AI techniques in transient stability assessment, highlighting methodologies and advantages over traditional methods.

Section 7: NN-BASED CONTROLLER
Description 7: This section discusses the use of neural networks in designing intelligent controllers for power systems, including the applications of supervised learning and reinforcement learning.

Section 8: NN-BASED WIDE-AREA CONTROL (WAC)
Description 8: This section reviews the application of neural networks in Wide-Area Control (WAC), discussing methodologies, challenges, and RL-based designs for system stabilization.

Section 9: NN-BASED TECHNIQUES IN MODERN POWER SYSTEM
Description 9: This section explores the role of neural networks in modern power systems, particularly in the control of renewable energy resources and ensuring rotor angle stability.

Section 10: CONCLUSION
Description 10: This section summarizes the findings, discusses the advantages and disadvantages of neural network approaches, and offers insights into future potential in power system stability and control.