TY  - EJOU
AU  - Xiao, Yong 
AU  - Jin, Xin 
AU  - Yang, Jingfeng 
AU  - Shen, Yanhua 
AU  - Guan, Quansheng 

TI  - A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2021
VL  - 126
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - 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

DO  - 10.32604/cmes.2021.012562