A leakage protection method for low-voltage power supply systems based on flexible regulation of the neutral point voltage

Wang, Zhan; Zhang, Xiangjun; Yu, Ke; Li, Zewen; Zhao, Zimo; Zhuo, Chao; Li, Li

doi:10.1016/j.ijepes.2023.109157

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Most of the low-voltage power grids in China are directly grounded at neutral points, and the consequences of leakage accidents are thus very serious [1,2]. Leakage protection is crucial to preventing the leakage fires and personal electric shock casualties in lowvoltage distribution networks [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network

Liu

Wei

2023

Applied Sciences

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The fluctuation of normal leakage current has a great influence on the fixed-threshold leakage protector. To address this issue, this paper proposes an adaptive leakage protection method based on the sparrow search algorithm (SSA)-backpropagation (BP) neural network. Based on the analysis of the normal leakage current generation mechanism, this method uses the SSA optimized BP neural network to construct a prediction model of normal leakage current. By dividing the normal leakage range into several intervals and setting the corresponding action threshold, the action threshold of the interval is automatically selected in advance, based on the predicted value of the model, so as to realize the adaptive protection of the leakage current faults. Experiments have proved that the leakage protector can identify the leakage fault more sensitively and increase the ratio of the protector put into operation by predicting the development of normal leakage current and adjusting the protection action threshold in advance.

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Section: Introductionmentioning

confidence: 99%

Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network

Liu

Wei

2023

Applied Sciences

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“…dielectric properties of the insulation layer will deteriorate due to the aging phenomenon, and timely detection of initial insulation faults before the permanent breakdown of insulation will help prevent the failure from developing into a major accident [2]. In terms of research on online insulation monitoring of power grids, the earliest research was carried out on insulation monitoring of lowvoltage distribution networks, which usually use leakage protection to monitor insulation status [3]. So far, researchers have proposed a variety of insulation monitoring methods.…”

mentioning

confidence: 99%

A Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation

Bai,

Liu,

Liu

et al. 2024

ENERGY

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Most ground faults in distribution network are caused by insulation deterioration of power equipment. It is difficult to find the insulation deterioration of the distribution network in time, and the development trend of the initial insulation fault is unknown, which brings difficulties to the distribution inspection. In order to solve the above problems, a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed. Firstly, the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network, and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system. Secondly, considering all kinds of fault factors of the distribution network and the influence of the power supply region, the evaluation method of the initial insulation fault situation of the distribution network is proposed, and the development situation of the distribution network insulation fault is classified according to the evaluation method. Then, principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data, and the support vector machine (SVM) was trained. The optimal parameter combination of the SVM model was found by the grid search method, and a multi-class SVM model based on 1-v-1 method was constructed. Finally, the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples. The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%, and the perception accuracy of 4 situation levels is above 96%. In addition, the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage, which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation. The correctness and effectiveness of this method are verified.

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Earth Fault Detection Method for TN-C-S Systems Based on Residual Current Shunt Coefficients

Sun,

Xu,

Wang

et al. 2024

2024 China International Conference on Electricity Distribution (CICED)

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A leakage protection method for low-voltage power supply systems based on flexible regulation of the neutral point voltage

Cited by 5 publications

References 15 publications

Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network

Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network

A Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation

Earth Fault Detection Method for TN-C-S Systems Based on Residual Current Shunt Coefficients

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