A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems

Cong Yang,Lu Wei,Zheng Qian,Yan Pei

doi:10.3390/en11082142

Cong Yang, Lu Wei + Show 2 more

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https://doi.org/10.3390/en11082142

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Abstract

With the rapid development of wind energy, it is important to reduce operation and maintenance (O&M) costs of wind turbines (WTs), especially for a pitch system, which suffers the highest failure rate and downtime. This paper proposes a data-driven method for pitch- system condition monitoring (CM) by only using supervisory control and data acquisition (SCADA) data without any faults, which could be applied to reduce O&M costs of pitch system by providing fault alarms. The pitch-motor temperature is selected as the indicator, and three feature-selection algorithms are employed to select the most appropriate input parameters for modeling. Six data-driven algorithms are applied to model pitch-motor temperature and the support vector regression (SVR) model has the highest accuracy. The control-chart method based on the residual errors between model output and measured value is utilized to calculate the outliers, thus the abnormal condition could be clearly identified once the outliers appear for a period of time. The effectiveness of the proposed method is demonstrated by several case studies, and compared with the classification models. Due to the adaptive ability and low cost, the proposed approach is suitable for online CM of pitch systems, and provides a strategy for CM of new WTs.

Highlights

As a commercially viable and environmentally sustainable energy source, wind energy has attracted sustained attention due to its abundance and high social benefits
Condition monitoring (CM) is an effective tool commonly employed to improve the reliability of wind turbines (WTs) and reduce operation and maintenance (O&M) costs
Four case studies have been analyzed to demonstrate the feasibility of the proposed approach, including a normal event and three pitch-system-failure events derived from four WTs, respectively

Summary

Introduction

As a commercially viable and environmentally sustainable energy source, wind energy has attracted sustained attention due to its abundance and high social benefits. The number of installed onshore and offshore wind farms is increasing to satisfy the rapidly growing demand. 2017, the cumulative installed wind-power capacity of China comprised approximately 188,392 MW, followed by USA, Germany, India, and Spain [1]. Costs are still high due to the harsh environment and the early deterioration of critical components. Condition monitoring (CM) is an effective tool commonly employed to improve the reliability of wind turbines (WTs) and reduce O&M costs. It allows the maintenance to be scheduled based on the conditions of WT components [2]

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