A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network

Xiange Tian, Yongjian Jiang, Chen Liang, Cong Liu, You Ying, Hua Wang, Dahai Zhang and Peng Qian
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Xiange Tian: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Yongjian Jiang: Ocean College, Zhejiang University, Hangzhou 310058, China
Chen Liang: Ocean College, Zhejiang University, Hangzhou 310058, China
Cong Liu: Southwest Technology and Engineering Research Institute, Chongqing 400039, China
You Ying: Zhejiang Windey Co., Ltd., Hangzhou 310012, China
Hua Wang: Huaneng Clean Energy Research Institute, Beijing 102209, China
Dahai Zhang: Ocean College, Zhejiang University, Hangzhou 310058, China
Peng Qian: Ocean College, Zhejiang University, Hangzhou 310058, China

Energies, 2022, vol. 15, issue 18, 1-15

Abstract: The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies.

Keywords: wind turbine; condition monitoring; SCADA data; GMDH neural network (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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