首页|期刊导航|南方电网技术|考虑多扰动因子的含光伏电源低压台区漏电故障检测

考虑多扰动因子的含光伏电源低压台区漏电故障检测OA北大核心CSTPCD

Leakage Fault Detection in Low-Voltage Station Area with Photovoltaic Power Supply Considering Multi-Disturbance Factors

中文摘要英文摘要

针对含光伏电源的低压配电台区剩余电流检测易受多重因素干扰,难以实现漏电故障精准检测的问题,提出了一种基于随机森林算法的计及剩余电流扰动因子含光伏电源的低压配电台区漏电故障检测方法.通过多角度挖掘分析剩余电流扰动因子,利用剩余电流偏差法定量分析剩余电流扰动因子对剩余电流的影响,分析计及剩余电流扰动因子的漏电故障频域特性,构建了多维故障特征向量与特征数据集,建立了基于随机森林算法的漏电故障检测模型.通过仿真模型进行仿真分析与验证,结果表明所提方法可高精度检测漏电故障,与常用方法相比,所提方法的故障检测准确率和稳定性更高、抗干扰能力更强.

Aiming at the problem that residual current detection in low-voltage distribution areas containing photovoltaic power sup-ply is easily affected by multiple factors and difficult to achieve accurate detection of leakage faults,a leakage fault detection method for low-voltage distribution areas containing photovoltaic power supply is proposed based on the random forest algorithm,taking into account the residual current disturbance factors.By mining and analyzing residual current disturbance factors from multiple perspec-tives,the residual current deviation method is used to quantitatively analyze the impact of residual current disturbance factors on residual current.The frequency domain characteristics of leakage faults considering residual current disturbance factors are analyzed,and multidimensional fault feature vectors and feature datasets are constructed.A leakage fault detection model based on random for-est algorithm is established.Through simulation analysis and verification using a simulation model,the results show that the proposed method can detect leakage faults with high accuracy.Compared with commonly used methods,the fault detection accuracy and stabil-ity of the proposed method are higher,and the anti-interference ability is stronger.

慕静茹;喻锟;曾祥君;仝海昕;罗晨;谢志成

电网防灾减灾全国重点实验室(长沙理工大学电气与信息工程学院),长沙 410114电网防灾减灾全国重点实验室(长沙理工大学电气与信息工程学院),长沙 410114电网防灾减灾全国重点实验室(长沙理工大学电气与信息工程学院),长沙 410114电网防灾减灾全国重点实验室(长沙理工大学电气与信息工程学院),长沙 410114电网防灾减灾全国重点实验室(长沙理工大学电气与信息工程学院),长沙 410114中国南方电网超高压输电公司,广州 510663

动力与电气工程

光伏电源低压配电系统剩余电流扰动因子随机森林算法漏电故障检测

photovoltaic power supplylow-voltage distribution systemresidual current disturbance factorrandom forest algorithmleakage fault detection

《南方电网技术》 2024 (10)

130-141,12

国家自然科学基金资助项目(52177070)湖南省自然科学基金资助项目(2021JJ30729)湖南省教育厅资助项目(22A0231). Supported by the National Natural Science Foundation of China(52177070)the Natural Science Foundation of Hunan Province(2021JJ30729)Hunan Provincial Department of Education Project(22A0231).

10.13648/j.cnki.issn1674-0629.2024.10.013

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