Abstract
Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.
Highlights
Wind turbines have witnessed a rapid growth in rating over the last 40 years [1]
It shows that the root mean square error (RMSE) values from Long Short-Term Memory (LSTM) models are approximately 4% lower than that from BP models for both cases, which means that LSTM models have better performance for condition monitoring than BP based models
A novel condition monitoring model of the wind turbine based on Long Short-Term
Summary
Wind turbines have witnessed a rapid growth in rating over the last 40 years [1]. they are usually required to operate in harsh environments, offshore [2,3]. Types, and numbers of wear particles indicate different degrees of wear or damage in the gearbox These above-mentioned methods require acceleration transducers and oil debris sensors leading to a high cost for implementing condition monitoring. Data-driven methods based on the temperature signal is effective in the monitoring of the gearbox. Adopting data-driven solutions is a good method to reduce the cost of the system, and it is desirable to design effective data-driven condition monitoring based on the temperature signal [4,15]. A novel condition monitoring method for wind turbines based on LSTM is proposed. MD method is applied to reduce the input variable number of the prediction model, which improves real time performance of the condition monitoring system.
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