Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (6): 54-63.doi: 10.16180/j.cnki.issn1007-7820.2022.06.009

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Research on Adaptability Evaluation of Distribution Network Based on Improved TOPSIS-PSO-SVM

HUANG Yuansheng,JIANG Yuqing,WANG Jing   

  1. Department of Economic Management,North China Electric Power University,Baoding 071003,China
  • Received:2021-01-17 Online:2022-06-15 Published:2022-06-20
  • Supported by:
    Natural Science Foundation of Hebei(F2020502009)


With the rapid development of renewable energy, new energy generation has become one of the main forces of power generation in China. In view of the adaptability of distribution network after distributed energy is connected to the grid, this study proposed a new definition of the adaptability of distribution network. A distribution network adaptability evaluation index system with six first-level indexes, including reliability, load rate, current, power quality, service life and new energy utilization rate is established. Through the subjective and objective weighting method, the combined weight is obtained. Combined with TOPSIS, the expected output value of the evaluation model is determined. This study proposes a distribution network adaptability evaluation model based on improved TOPSIS-PSO-SVM. A distribution network adaptability evaluation model based on improved TOPSIS-PSO-SVM is proposed, and the distribution network in 5 regions of Ningxia is used for example analysis. The results show that the evaluation relative error interval of the TOPSIS-PSO-SVM evaluation model is [0.94%, 1.03%], and the average absolute value of the relative error is 0.885 4%, which indicates that the evaluation model has smaller evaluation error and higher evaluation precision in the adaptability evaluation of distribution network.

Key words: distribution network, distributed energy, adaptability evaluation, analytic hierarchy process, entropy method, combination weight, ideal point method, improved particle swarm optimization support vector machine

CLC Number: 

  • TP13