张新鹤,何桂雄,梁琛,马喜平,何振武,姜飞
ZHANG Xinhe,HE Guixiong,LIANG Chen,MA Xiping,HE Zhenwu,JIANG Fei

• 1:中国电力科学研究院有限公司
• 2:国网甘肃省电力公司电力科学研究院
• 3:长沙理工大学电气与信息工程学院

摘要(Abstract)：

针对配电网线损管理中基础数据异常和冗余量大的问题，提出基于分割区域的配电网异常线损数据辨识与修正方法。考虑终端数据存在冗余量，利用卡尔曼滤波算法对终端冗余数据进行融合，再遍历配电网各线路节点配电变压器，采用局部异常因子算法检测运行数据；基于配电网拓扑关系，采用GN(Girvan-Newman)算法对异常节点进行区域分割；通过分析分割区域邻近节点量测数据和不平衡度指标，动态调整区域边界，直到分割区域满足估计的可观性条件，得到分割区域最终划分结果，并基于区域内节点量测模型、约束模型和估计模型求解异常数据。最后，以西北某省10 kV什新线、什金线为算例进行分析验证，结果表明所提方法可有效实现配电网异常线损数据的辨识及修正。
Given the basic data anomalies and significant redundancy in line loss management within distribution networks, a technique for identifying and correcting abnormal line loss data based on segmented regions is proposed.In view of the redundancy of terminal data, a Kalman filter algorithm is employed to fuse terminal redundant data.Then, by traversing the distribution transformers of various line nodes in the distribution network and using local outlier factor(LOF) algorithm, the operational data are detected. Based on the topological relationship of the distribution networks, the Girvan-Newman(GN) algorithm is used to segment the abnormal nodes. By analyzing the neighboring node measurement data and the imbalance index of the segmented regions, the boundary of the regions is dynamically adjusted until the segmented regions meet the estimated observability conditions. The final division result of segmented regions is obtained, and abnormal data are solved using the measurement model, constraint model, and estimation model within the regions. Finally, an example of the 10 kV Shixin line and Shijin line in a province in Northwest China is used to validate the proposed method. The results demonstrate that the proposed method can identify and correct abnormal line loss data within distribution networks.

关键词(KeyWords)： 线损数据;GN算法;局部异常因子;分割区域;卡尔曼滤波
line loss data;GN algorithm;LOF;segmented region;Kalman filter

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司科技项目（52272221N002）

作者(Author): 张新鹤,何桂雄,梁琛,马喜平,何振武,姜飞
ZHANG Xinhe,HE Guixiong,LIANG Chen,MA Xiping,HE Zhenwu,JIANG Fei

DOI: 10.19585/j.zjdl.202310011

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