Study on the operating condition diagnosis method of hydropower station equipment based on fuzzy clustering analysis

• Published in: Journal of physics. Conference series 2022-07, Vol.2303 (1), p.12079
• Main Authors: Zhao, Yuzhong, Tang, Jian, Wang, Jun, Wang, Jie, Qiu, Mianqian
• Format: Article
• Language: English
• Subjects: Algorithms; Cluster analysis; Clustering; Coders; Diagnosis; Diagnostic systems; Error analysis; Hydroelectric power; Hydroelectric power stations; Physics; Search algorithms
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2303/1/012079

The current operation status diagnosis of hydropower station equipment is mainly based on the independent analysis of the equipment, ignoring the correlation between the equipment, resulting in a large diagnostic error. In order to improve the above problems, a diagnosis method based on fuzzy clustering analysis is studied. This paper deals with association analysis and error data discrimination of equipment state data in operation collection of hydropower station, and selects operation data preliminarily. Using gravitational search algorithm for fuzzy c-means algorithm of the initial fuzzy membership degree matrix, improved fuzzy clustering algorithm is used to analyze the running status data clustering at a time. After using the encoder to extract data, secondary cluster, the abnormal data and equipment state match, the final diagnosis results are obtained. In the case verification experiment, the diagnostic accuracy rate of the method is as high as 94.83%, and the diagnostic efficiency is higher and the real-time performance is better.