Affiliations: [a] College of Energy Power and Mechanical Engineering, North China Electric Power University, ChangPing District, Beijing, China
| [b] Department of Cardiovascular Internal Medicine, Nanlou Branch of Chinese PLA General Hosptal, Beijing, China
| [c] School of Automation, Beijing Institute of Technology, Beijing, China
| [d] School of Control and Computer Engineering, North China Electric Power University, ChangPing District, Beijing, China
Correspondence:
[*]
Corresponding author. Shuli Guo, School of Automation, Beijing Institute of Technology, Beijing 100081, China. E-mail: guoshuli@bit.edu.cn.
Note: [1] The first two authors contributed equally to this manuscript.
Abstract: This paper presented an improved fuzzy synthetic model based on combination weighting and a cloud model to optimize wind turbine maintenance strategy and improve operational reliability. First, a condition assessment framework was proposed by analyzing the monitored physical quantities of a working wind turbine. Based on the establishment of a state health evaluation index and health status classification of wind turbines, the weight of each index was determined with a combination weighting method while the membership degree of each state grade was determined with a membership cloud model. A comprehensive evaluation of the health status of the wind turbine was carried out using the method of stratified evaluation. The results showed that the proposed method was effective and feasible. The results also showed that the condition assessment that utilized the improved method predicted the change of operating conditions and more closely matched real operating conditions than the traditional fuzzy assessment method.
