Open Access

ARTICLE

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

Yong Xiao¹, Xin Jin¹, Jingfeng Yang², Yanhua Shen^3,*, Quansheng Guan⁴
1 China Southern Power Grid Research Institute Co., Ltd., Guangzhou, 510663, China
2 China Southern Power Grid Co., Ltd., Guangzhou, 510663, China
3 School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510641, China
4 School of Electronics and Information, South China University of Technology, Guangzhou, 510641, China
* Corresponding Author: Yanhua Shen. Email:
(This article belongs to this Special Issue: Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce)

Computer Modeling in Engineering & Sciences 2021, 126(3), 1293-1313. https://doi.org/10.32604/cmes.2021.012562

Received 04 July 2020; Accepted 20 August 2020; Issue published 19 February 2021

Abstract

User-transformer relations are significant to electric power marketing, power supply safety, and line loss calculations. To get accurate user-transformer relations, this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization (QPSO) and Fuzzy C-Means Clustering. The main idea is: as energy meters at different transformer areas exhibit different zero-crossing shift features, we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations. The proposed method contributes in three main ways. First, based on the fuzzy C-means clustering algorithm (FCM), the quantum particle swarm optimization (PSO) is introduced to optimize the FCM clustering center and kernel parameters. The optimized FCM algorithm can improve clustering accuracy and efficiency. Since easily falls into a local optimum, an improved PSO optimization algorithm (IQPSO) is proposed. Secondly, considering that traditional FCM cannot solve the linear inseparability problem, this article uses a FCM (KFCM) that introduces kernel functions. Combined with the IQPSO optimization algorithm used in the previous step, the IQPSO-KFCM algorithm is proposed. Simulation experiments verify the superiority of the proposed method. Finally, the proposed method is applied to transformer detection. The proposed method determines the class members of transformers and meters in the actual transformer area, and obtains results consistent with actual user-transformer relations. This fully shows that the proposed method has practical application value.

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Keywords

User-transformer relation identification; zero-crossing shift; fuzzy C-means clustering; quantum particle swarm optimization; attractor multiple update strategy; dynamic crossover strategy; perturbation strategy of potential-well characteristic length

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