Fault Detection Based on a Combined Approach of FA-CP-ELM with Application to Wind Turbine System
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ORIGINAL ARTICLE
Fault Detection Based on a Combined Approach of FA‑CP‑ELM with Application to Wind Turbine System Wenxin Yu1,2 · Shoudao Huang1 · Junnian Wang3 Received: 12 June 2019 / Revised: 14 July 2020 / Accepted: 24 September 2020 © The Korean Institute of Electrical Engineers 2020
Abstract In this paper, a novel wind turbine (WT) fault detection method, based on the Partial Least Squares (PLS), Firefly Algorithm (FA), Chaos Map (CP) and Extreme Learning Machine (ELM), which is proposed and explained in detail. The proposed method includes two procedures: a WT mathematical model with PLS and a prediction model with FA-CP-ELM. Since the WT system is modeled as a system using PLS, the ELM has been optimized by the FA and CP to improve the predictive performance. Then, it’s calculated the residual between the mathematical model and the predicted model. If a fault occurs, the residual will increase accordingly and exceed the tolerance range. Hence, a fault can be detected quickly. To demonstrate the feasibility and effectiveness of the proposed approach, the wind turbine system is tested with a fault point set in this system. According to the results of the example, this proposed method is found to achieve better performance. Keywords Wind turbine · Firefly algorithm · Chaos map · Extreme learning machine · Faults detection · Partial least squares
1 Introduction With the deepening of the global energy crisis and the intensification of atmospheric pollution, the development and utilization of new energy sources such as wind and solar energy have received more and more attention. As a new technology mature energy, wind energy has developed rapidly in the past few decades. Because large wind turbines are mostly located in the outer suburbs and offshore, the daily operation status is difficult to be detected, and the cost of the operation and maintenance is so high. According to statistics, for onshore wind power equipment, the cost of operation is generally accounted on 15–20% in the total revenue of wind farms; * Wenxin Yu [email protected] 1
College of Electrical and Information Engineering, Hunan University, Changsha 410082, Hunan, People’s Republic of China
2
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, People’s Republic of China
3
School of Physics and Electronics, Hunan University of Science and Technology, Xiangtan 411201, Hunan, People’s Republic of China
for offshore wind power, the cost of operation and maintenance accounts for from 25–35% in the total revenue. As the single-unit capacity of wind turbines increased, the wind turbine becomes an increasing in complication. The failure rate and maintenance cost of the wind turbine is becoming much higher [1]. In order to reduce the failure rate of wind turbines and decrease the operation and maintenance costs, it is necessary to carry out state monitoring and fault diagnosis research of wind turbines, timely grasped the operating status of wind turbines, detected
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