Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced
  Smart Distribution Grids
Deepak Tiwari Mehdi Jabbari Zideh Veeru Talreja Vishal Verma Sarika K. Solanki Jignesh Solanki
Most power systems' approaches are currently tending towards stochastic and
probabilistic methods due to the high variability of renewable sources and the
stochastic nature of loads. Conventional power flow (PF) approaches such as
forward-backward sweep (FBS) and Newton-Raphson require a high number of
iterations to solve non-linear PF equations making them computationally very
intensive. PF is the most important study performed by utility, required in all
stages of the power system, especially in operations and planning. This paper
discusses the applications of deep learning (DL) to predict PF solutions for
three-phase unbalanced power distribution grids. Three deep neural networks
(DNNs); Radial Basis Function Network (RBFnet), Multi-Layer Perceptron (MLP),
and Convolutional Neural Network (CNN), are proposed in this paper to predict
PF solutions. The PF problem is formulated as a multi-output regression model
where two or more output values are predicted based on the inputs. The training
and testing data are generated through the OpenDSS-MATLAB COM interface. These
methods are completely data-driven where the training relies on reducing the
mismatch at each node without the need for the knowledge of the system. The
novelty of the proposed methodology is that the models can accurately predict
the PF solutions for the unbalanced distribution grids with mutual coupling and
are robust to different R/X ratios, topology changes as well as generation and
load variability introduced by the integration of distributed energy resources
(DERs) and electric vehicles (EVs). To test the efficacy of the DNN models,
they are applied to IEEE 4-node and 123-node test cases, and the American
Electric Power (AEP) feeder model. The PF results for RBFnet, MLP, and CNN
models are discussed in this paper demonstrating that all three DNN models
provide highly accurate results in predicting PF solutions.