Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance

arXiv:2403.00975v1 Announce Type: new
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 …

abstract advanced analysis arxiv assessment cs.ai cs.lg data diverse ensemble equipment functional health innovation key lies long short-term memory lstm math.fa memory networks neural networks performance power series stat.ap study time series type wind wind turbines