基于多维波形差异度聚类分析的配电网故障区段定位方法OA北大核心CSTPCD
Fault Section Location Method for Distribution Network Based on Multi-Dimensional Waveform Difference Clustering Analysis
谐振接地系统运行环境繁杂,单相接地故障发生状态多变,基于单一特征量的传统配电网故障区段定位方法存在各自的定位判别盲区,方法可靠性和适用性难以保障.针对上述问题提出了一种无需整定的基于多维波形差异度聚类分析的配电网故障区段定位方法.依据故障点上下游各检测点零序电流暂态及稳态分量的波形差异性特征,通过离散Fréchet距离算法量化相邻检测点间的暂态及稳态波形差异度,并采用模糊C均值算法(fuzzy C-means,FCM)综合多特征信息以区分正常区段与故障区段,有效消除了单一特征定位盲区,实现故障区段自适应准确定位.PSCAD/EMTDC仿真结果以及10 kV真型实验结果表明所提方法不仅保证了在不同故障工况下的定位可靠性和适用性,还具有耐高阻能力强、抗噪性高等优点.
The resonant grounding system has a complex operating environment and variable single-phase grounding fault occurrence status.The distribution network traditional fault section location methods based on a single characteristic quantity have their own location blind zones,and the reliability and applicability of methods are also difficult to be guaranteed.To solve the above problems,a fault section location method for distribution network based on multi-dimensional waveform difference clustering analysis without tuning is proposed.According to the waveform difference characteristics of the zero-sequence current transient and steady-state components at each detection point upstream and downstream of the fault point,the transient and steady-state waveform differences between adjacent detection points are quantified by discrete Fréchet distance algorithm,and fuzzy C-means(FCM)is used to synthesize multi-feature information to divide the normal section and fault section.The location blind zones with single feature are eliminated effectively and the accurate fault section location is realized adaptively.The simulation results of PSCAD/EMTDC and 10kV true experimental test show that the proposed method not only ensures reliability and applicability under different fault condi-tions,but also has high resistance resistance and noise resistance.
罗晗菁;曾祥君;喻锟;李志;谢志成;邓军
智能电网运行与控制湖南省重点实验室(长沙理工大学),长沙 410114浙江华电器材检测研究院有限公司,杭州 311217中国南方电网有限责任公司超高压输电公司,广州 510663
动力与电气工程
谐振接地系统故障定位零序电流离散Fréchet距离模糊C均值算法(FCM)聚类
resonant grounding systemfault locationzero sequence currentdiscrete Fréchet distancefuzzy C-means clustering
《南方电网技术》 2024 (006)
58-68,97 / 12
国家自然科学基金资助项目(51737002,52177070);湖南省自然科学基金项目(2021JJ30729);湖南省教育厅项目(22A0231);湖南省研究生科研创新项目(CX20210792);国网浙江省电力公司科技项目(2021FD05). Supported by the National Natural Science Foundation(51737002,52177070);Hunan Provincial Natural Science Foundation of China(2021JJ30729);Hunan Provincial Education Department Project(22A0231);Hunan Province Postgraduate Research and Innovation Project(CX20210792);the Science and Technology Project of State Grid Zhejiang Electric Power Corporation(2021FD05).
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