A Novel Health State Assessment Method of Low-voltage Distribution Network Based on CV-G2-ER Rule
DOI:
https://doi.org/10.4108/ew.14215Keywords:
low-voltage distribution network, health state assessment, data reliability, evidential reasoning rule, coefficient of variation methodAbstract
INTRODUCTION: As the distribution network segment closest to end users, the health status of low-voltage distribution networks directly affects the quality of people's production and daily life. Aiming to address the key limitations existing in the health status evaluation of low-voltage distribution networks—including the reliability of evaluation data, weight determination of the evaluation index system, and uncertainty involved in the evaluation process—this paper proposes a novel health status evaluation method for low-voltage distribution networks based on the CV-G2-ER rule.
METHODS: The proposed method is implemented through three core standardized steps. First, the reliability of attribute evaluation data is calculated based on the distance metric between evaluation datasets. Second, the objective weight and subjective weight of the evaluation index system are calculated using the Coefficient of Variation (CV) method and G2 method, respectively; the comprehensive weight is then obtained by fusing the two weights via the Lagrange multiplier method, which effectively improves the rationality of the index system's weight assignment. Finally, multi-attribute evaluation information is fused based on the Evidential Reasoning (ER) rule, to derive the final health status evaluation conclusion and corresponding confidence level of the low-voltage distribution network.
RESULTS: The effectiveness and feasibility of the proposed method are verified via a case study, which covers the health status evaluation and comparative analysis of 10 low-voltage distribution networks under the jurisdiction of a regional power supply bureau.
CONCLUSION: The proposed CV-G2-ER rule-based evaluation method effectively resolves the core pain points in the current health status evaluation process of low-voltage distribution networks, and provides a practical, scientific and reliable technical approach for the health management and operation optimization of low-voltage distribution systems.
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[1] Liu D, Timmerman S, Xiang Y, Hosseini E, Palensky P, Vergara PP. "A data-driven approach for topology correction in low voltage distribution networks with photovoltaics." Sustainable Energy, Grids and Networks. 2026;doi: https://doi.org/10.1016/j.segan.2026.102321.
[2] Zhenguo Liu, Zhidong Jia, Ming Ma, et al. "Data-driven photovoltaic hosting capacity calculation method for unbalanced low voltage distribution networks." Electrical Power and Energy Systems. 2026;176:1-12.
[3] QIU Shengmin, GUAN Lin. "Simplification of distribution network planning and its reliability evaluation algorithm." Electric Power Automation Equipment. 2013;33.1:85-90.
[4] LIU Jian, HAN Lei, ZHANG Zhihua. "Customer-oriented distribution network operational risk assessment considering urgency." Electric Power Automation Equipment. 2015;35.2:97-102, 109.
[5] ZHAO Huiru, LI Nana, GUO Sen, et al. "Real-time assessment of power failure risk of power distribution network equipment." Electric Power Automation Equipment. 2014;34.11:89-94.
[6] MA Ji, LIU Xizhe. "Health state assessment of low-voltage distribution network station area based on G2-entropy weight method." Electric Power Automation Equipment. 2017;37.1:41-46.
[7] ZOU Yunfeng, MEI Fei, LI Yue, et al. "Research on the reasonable line loss prediction model in Taiwan area based on data mining technology." Power Demand Side Management. 2015;17.4:25-29.
[8] OU yang Sen, CHEN Xinhui, GENG Hongjie. "Evaluation of voltage characteristics of low-voltage distribution network stations based on efficacy coefficient method." Journal of South China University of Technology (Natural Science Edition). 2015;43.8:35-40.
[9] LI Xiaohui, ZHANG Lai, LI Xiaoyu, etc. "Research on the evaluation method of current power grid based on analytic hierarchy process." Power System Protection and Control. 2008;36.14:57-61.
[10] QIN Dandan, ZHENG Gaofeng, LIU Li, LI Longyue, LIANG Xiaowei, SUN Wei. "Comprehensive evaluation and analysis method for the health state of low-voltage distribution network stations." Information Technology. 2021;1.10:149-155.
[11] HE Shan, WANG Wenda, ZHANG Wei. "Evaluation method of low-voltage distribution network operating state based on data mining." Guangdong Electric Power. 2019;32.5:80-86.
[12] XUE Shiwei, JIA Qingquan, ZHANG Kexin, GAO Zhiqiang, LIANG Jifeng, LI Yang. "Stochastic Modelling and Unbalance Evaluation of Low-Voltage Distribution Network Load Driven by Electricity Data." Automation of Electric Power Systems. 2022;1.1:1-14.
[13] ZHOU Zhijie, Hu Guangyu, et al. "A model Forbidden behaviour prediction of complex systems based on belief rule base and power set." IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018;48.9:1649-1655.
[14] HAN D Q, YANG Y, HAN C Z. "Advances in DS evidence theory and related discussions." Control and Decision. 2014;29.1:7-77.
[15] CHANG I. L, ZHOU Z J, et al. "Belief rule based expert system for classification problems with new rule activation and weight calculation procedures." Information Science. 2016;336.C:75-91.
[16] TANG Shuaiwen, et al. "Consensus evaluation of UAV swan cooperative situation awareness considering perturbation." Acta Aeronautics et Astronautics Sinica. 2021;41.S2.:724233.
[17] FENG Zhichao, ZHOU Zhijie, et al. "A fault diagnose and tolerant control method for aerospace relay with environmental diamante (in Chinese)." Sci Sin Inform. 2021;51.1:648-662.
[18] Shanshan Liu, Liang Chang, Guanyu Hu, Shiyu Li. "A Novel Evidential Reasoning Rule with Causal Relationships between Evidence." Computers, Materials & Continua. 2025;85.1:1113–1134.
[19] Chao Fu, Wenjun Chang, Min Xue, Guangyan Lu. "A data-driven open decision framework based on adaptive evidential reasoning rule." Computers & Industrial Engineering. 2025;206:111247.
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