Equipment Health Assessment: Time Series Analysis for Wind Turbine
  Performance
Jana Backhus Aniruddha Rajendra Rao Chandrasekar Venkatraman Abhishek Padmanabhan A. Vinoth Kumar Chetan Gupta
In this study, we leverage SCADA data from diverse wind turbines to predict
power output, employing advanced time series methods, specifically Functional
Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key
innovation lies in the ensemble of FNN and LSTM models, capitalizing on their
collective learning. This ensemble approach outperforms individual models,
ensuring stable and accurate power output predictions. Additionally, machine
learning techniques are applied to detect wind turbine performance
deterioration, enabling proactive maintenance strategies and health assessment.
Crucially, our analysis reveals the uniqueness of each wind turbine,
necessitating tailored models for optimal predictions. These insight
underscores the importance of providing automatized customization for different
turbines to keep human modeling effort low. Importantly, the methodologies
developed in this analysis are not limited to wind turbines; they can be
extended to predict and optimize performance in various machinery, highlighting
the versatility and applicability of our research across diverse industrial
contexts.