★ 國網南通供電公司 曹藝洋

摘要： 當前配電網故障診斷體系存在診斷延時過長的問題，傳統依賴GIS平臺的方法在大規模復雜數據場景下難以兼顧實時性與準確性。針對這一問題，本文提出了一種基于數據挖掘的混合診斷方法。該方法利用mPMU數據構建故障樣本集，通過KICIC聚類實現故障類型劃分，并結合粗糙集屬性約簡與改進LM-BP神經網絡進行快速識別。實驗結果表明，與傳統GIS方法相比，該方法在保證診斷準確率的同時，將平均響應時延從約160ms縮短至95ms，最大延時壓縮至120ms以內，整體耗時降低約40%。該研究有效驗證了數據挖掘技術在提升配電網故障診斷實時性方面的優勢，為電網快速隔離故障與恢復供電提供了可靠支撐。

關鍵詞：數據挖掘；配電網故障；故障診斷；診斷分析

Abstract: Current fault diagnosis systems for distribution networks suffer from excessive diagnostic latency. Traditional methods relying on GIS platforms struggle to balance real-time performance and accuracy when handling large-scale, complex data scenarios. To address this challenge, this paper proposes a hybrid fault diagnosis method based on data mining techniques. The method constructs fault sample sets using μPMU data, classifies fault types via KICIC clustering, and integrates rough set attribute reduction with an improved LM- BP neural network for rapid identification. Experimental results demonstrate that, compared to conventional GIS-based approaches, the proposed method maintains diagnostic accuracy while reducing the average response delay from approximately 160 ms to 95 ms, with maximum latency compressed to within 120 ms—an overall reduction of approximately 40%. This study effectively validates the advantages of data mining techniques in enhancing the real-time performance of distribution network fault diagnosis, providing reliable support for rapid fault isolation and service restoration in power grids.

Key words: Data mining; Distribution network fault; Fault diagnosis; Diagnosis analysis

在線預覽：數據挖掘技術在配電網故障診斷中的應用.pdf

摘自《自動化博覽》2026年7月刊