Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance

Abstract

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.

MoCoformer: Quantifying Temporal Irregularities in Solar Wind for Long-Term Sequence Prediction

“…However, these methods are mainly applicable to linear and smooth time series, making it difficult to deal with non-linear and non-smooth series. To overcome this problem, recurrent neural networks (RNNs) [36], especially popular ones such as long shortterm memory (LSTM) [37] and gated recurrent unit (GRU) [38], have been introduced in deep learning variants, performing excellently in time series forecasting, Backhus et al [39] predicted the power output of different wind turbine data, which were calculated using techniques such as LSTM. Additionally, RNN models based on attention mechanisms are widely used in time series forecasting tasks.…”