Title: Integrated transformer condition assessment based on SOM neural network evidence cloud element model

Authors: Lingzhi Yi; Huang Yu; Yahui Wang; Xunjian Xu; Haixiang She; Xinren Su; Ganlin Jiang

Addresses: College of Automation and Electronic Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China ' College of Automation and Electronic Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China ' College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, 410082, China ' State Key Laboratory of Disaster Prevention and Mitigation for Power Grid Transmission and Substation Equipment, Changsha, Hunan, 410129, China ' Pu'er Infrastructure Section of China Railway Kunming Group Co., Ltd., Kunming, Yunnan 665000, China ' College of Automation and Electronic Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China ' College of Automation and Electronic Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China

Abstract: Aiming at some problems in the process of transformer evaluation, a comprehensive transformer state evaluation method based on SOM neural network evidence cloud object element model is proposed. Firstly, the random factor optimisation combination weights are adopted to solve the problem of expert randomness and incomplete data characteristics of current weight calculation method. Secondly, filtering algorithms and threshold learning mechanisms are introduced to optimise the SOM neural network for clustering. It can solve the problems of randomness and subjectivity in the previous transformer evaluation class intervals. Thirdly, improving the D-S evidence theory based on Pearson's correlation coefficient. It can fuse different characteristic indicators of transformers to avoid contradiction when fusing high conflict evidence. Lastly, the cloud entropy optimisation algorithm applies to improve the cloud object element model to determine the final assessment results. Take 6 actual transformers as an example. It proves the effectiveness and accuracy of the method, and applies to oil-immersed power transformers of different service periods.

Keywords: oil-immersed power transformers; comprehensive status assessment; improving SOM neural network; improved cloud object metamodel; Pearson correlation coefficient; D-S theory of evidence.

DOI: 10.1504/IJAMECHS.2024.139182

International Journal of Advanced Mechatronic Systems, 2024 Vol.11 No.2, pp.73 - 94

Received: 21 Sep 2023
Accepted: 27 Dec 2023
Published online: 24 Jun 2024 *

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