A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Peng Qian, Xiange Tian, Jamil Kanfoud, Joash Lap Yan Lee and Tat-Hean Gan
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Peng Qian: Brunel Innovation Centre, Brunel University, London UB8 3PH, UK
Xiange Tian: Brunel Innovation Centre, Brunel University, London UB8 3PH, UK
Jamil Kanfoud: Brunel Innovation Centre, Brunel University, London UB8 3PH, UK
Joash Lap Yan Lee: Brunel Innovation Centre, Brunel University, London UB8 3PH, UK
Tat-Hean Gan: Brunel Innovation Centre, Brunel University, London UB8 3PH, UK

Energies, 2019, vol. 12, issue 18, 1-15

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.

Keywords: wind turbine; condition monitoring; long short-term memory; SCADA; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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