首页|基于改进神经网络的10kV配电网相间短路故障自动化检测系统

基于改进神经网络的10kV配电网相间短路故障自动化检测系统

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为提高10 kV配电网相间短路故障自动化检测精度,设计了一种基于改进神经网络的10 kV配电网相间短路故障自动化检测系统.该系统由在线监测模块的探头装置,检测10 kV配电网线路电压、电流信号信息,通过短距离无线网与通信模块GPRS网络连接,发送至数据分析模块;数据分析模块由服务器、计算机端构成,电压、电流信号数据会实时缓存于服务器中,由计算机端使用基于改进神经网络的相间短路故障诊断模型,诊断电压、电流信号样本,是否属于10 kV配电网相间短路故障状态,完成10 kV配电网相间短路故障自动化检测.通过实验验证,该系统在10 kV配电网相间短路故障检测方面具有很高的准确性,无须人工操作,具有广泛的应用前景.
Automated Detection System for Interphase Short Circuit Fault in 10 kV Distri-bution Network Based on Improved Neural Network
To improve the accuracy of automatic detection of phase to phase short circuit faults in 10 kV distribution networks,an improved neural network based automatic detection system for phase to phase short circuit faults in 10 kV distribution networks is designed.The system consists of a probe device in the online monitoring module,which de-tects the voltage and current signal information of the 10 kV distribution network line.It is connected to the com-munication module GPRS network through a short distance wireless network and sent to the data analysis module.The data analysis module is composed of a server and a computer.The voltage and current signal data are cached in real-time in the server.The computer uses an improved neural network-based interphase short circuit fault diagnosis model to diagnose voltage and current signal samples,determine whether they belong to the 10 kV distribution network interphase short circuit fault state,and complete the automatic detection of 10 kV distribution network interphase short circuit faults.Through experimental verification.This system has high accuracy in detecting phase to phase short cir-cuit faults in 10 kV distribution networks,without manual operation,and has broad application prospects.

improving neural networks10 kV distribution networkshort circuit between phasesfault automationde-tection systemwhale optimization algorithm

徐威、王福坤、辛科、王一帆

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宁波送变电建设有限公司带电作业分公司,宁波 315000

改进神经网络 10kV配电网 相间短路 故障自动化 检测系统 鲸鱼优化算法

宁波永耀电力投资集团公司2022-2023年度产业单位科技项目

KJXM2022020

2024

自动化与仪表
天津市工业自动化仪表研究所 天津市自动化学会

自动化与仪表

CSTPCD
影响因子:0.548
ISSN:1001-9944
年,卷(期):2024.39(2)
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