NDLI: Transformer fault diagnosis based on improved fuzzy ISODATA algorithm

Content Provider	IEEE Xplore Digital Library
Author	Zheng Xiao-Li Wang Shen-Qiang Cao Yan-Zhao Wei Lei-Yuan
Copyright Year	2014
Description	Author affiliation: Beijing SiFang Autom. Co. Ltd., Beijing, China (Wang Shen-Qiang; Wei Lei-Yuan) \|\| Maoming Power Supply Bur., Guangdong Power Grid Co., Maoming, China (Zheng Xiao-Li; Cao Yan-Zhao)
Abstract	Dissolved Gas Analysis (DGA) has already gained its popularity in fault diagnosis for the oil-immersed transformers. However, owing to the fuzziness and uncertainty between the failure phenomena and failure mechanisms, power equipment failure reasons are very complicated and the accuracy of existing algorithms is low. Diagnostic methods based on artificial intelligence are commonly introduced in the field above. Because of the complexity of the network, the speed of the algorithm convergence is badly affected. With the limitation of artificial guidance and expertise, the current algorithms are short of the self-learning ability. That is why there is no common diagnostic program can be formed. Based on this reason, the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) based on DGA is proposed. First, the feasibility of transformer fault diagnosis method based on the ISODATA and DGA are analyzed, as well as its limitations. In order to improve the efficiency of the algorithm, a genetic algorithm is introduced to optimize the transformer fault diagnosis model by reducing the dependence of the initial clustering for ISODATA based on DGA. Through these methods, the accuracy and efficiency of optimizing diagnosis are improved. With the analysis of the principles of fuzzy ISODATA algorithm and genetic algorithm, and the optimization the initial cluster centers on ISODATA algorithm, the feasibility of the optimized transformer fault diagnosis program is proved. Finally, a specific case is programmed and compared to prove its accuracy and efficiency by analysis and comparing indicators before and after improvement. It is shown in the experimental comparison that the number of iterations is less after the improvement with the same precision and the operating speed is faster with the less error. The results showed that the fuzzy ISODATA algorithm optimized by the genetic algorithms is more in line with actual needs by largely overcoming the dependence on initial cluster center and can be easily applied to oil-immersed transformer fault diagnosis.
Starting Page	1279
Ending Page	1286
File Size	656081
Page Count	8
File Format	PDF
ISBN	9781479950324
DOI	10.1109/POWERCON.2014.6993582
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Oil insulation Algorithm design and analysis Genetic algorithms Clustering algorithms Fault diagnosis Power transformer insulation clustering optimization transformer fault diagnosis fuzzy ISODATA genetic algorithm DGA
Content Type	Text
Resource Type	Article

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