DOI: https://doi.org/10.15407/techned2020.06.047

COMPREHENSIVE METHOD FOR EVALUATION OF MEDIUM- AND LOW-VOLTAGE DISTRIBUTION NETWORK OPERATING STATE

Authors
Shiwei Su^1,2, Yiran You², Yu Zou³
1- China Three Gorges University Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station,
Yichang 443002, China
2- China Three Gorges University College of Electrical Engineering & New Energy,
Yichang 443002, China
3- Qinzhou Power Supply Bureau of Guangxi Power Grid Co., Ltd.,
Qinzhou 535000, China
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Abstract

With the development of intelligent distribution networks and access to distributed energy, the solving the problem of timely and accurate determination of the operating state of the distribution network is an urgent task. Based on an improved analysis of the principle components of the network and statements of a self-organizing neural network, this article proposes the method to evaluate the operating state of medium- and low-voltage distribution networks. At the first step, the system of evaluating indices of the network is formed by advanced component analysis. The evaluation system is grounded on four aspects, including safety, reliability, quality and economy. Next, the self-organizing neural network is used to identify and clean up the data regarding the operating state of the distribution network. At the next step, the indicators are modeled at all levels; the entropy method is applied to calculate the total weight of all indicators. Then the value of each indicator is found and the weak links of the distribution network are determined. At the final stage, the comprehensive assessment of the real operation of the distribution network in Guangxi province is carried out. As shown, the method can effectively reduce the effect of abnormal data and subjectivity factor on the results of evaluating the state of the distribution network. That confirms the feasibility and practicability of the proposed method. References 22, figures 6, tables 6.

Key words: distribution network, improved principal component analysis, self-organizing neural network, entropy combination, comprehensive evaluation.

Received: 10.01.2020
Accepted: 13.07.2020
Published: 21.10.2020

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