Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
This research addresses equipment health assessment for wind turbine operators, offering incremental improvements through ensemble methods and customization for individual turbines.
The study tackled wind turbine performance prediction using SCADA data, employing an ensemble of Functional Neural Networks and Long Short-Term Memory networks that outperformed individual models for accurate power output predictions, and applied machine learning to detect performance deterioration for proactive maintenance.
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.