| 引用本文: | 陈光宇,张盛杰,杨 里,等.基于多维场景划分的台区线损率异常研判及关联用户精准追踪方法[J].电力系统保护与控制,2024,52(16):162-177.[点击复制] |
| CHEN Guangyu,ZHANG Shengjie,YANG Li,et al.Station line loss rate anomaly identification and accurate tracking method of associated users based on multi-dimensional scene division[J].Power System Protection and Control,2024,52(16):162-177[点击复制] |
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| 摘要: |
| 针对台区线损率异常辨识困难，关联用户难以精准追踪的问题，提出一种基于多维场景划分的台区线损率异常研判及关联用户精准追踪方法。首先，提出了一种基于改进完备集合经验的模态分解(improved complete EEMD with adaptive noise, ICEEMDAN)算法的数据预处理策略，通过分析台区历史线损率曲线的变化趋势，构建台区线损率粗放场景集，并在此基础上采用支持向量聚类算法进一步对场景集进行二次精细划分，实现对台区多维场景集的建立。其次，提出一种基于簇类个案数目的区间动态平移策略，用于确定不同场景下线损率标准库的区间范围，并采用区间重叠率策略对划分中的孤立区间进行合并，实现对台区线损率标准库的完备划分。最后，给出一种基于关联分析的线损异常追踪方法，通过灰色关联分析法确定线损异常强相关因素，并基于改进Adtributor算法定量分析各因素和台区用户间的内在关联度，提高对台区线损率异常用户的追踪精度。算例采用台区真实数据进行仿真分析，结果表明了所提方法的有效性和实用性。 |
| 关键词: 台区 灰色关联 多维场景集 线损率标准库 精准追踪 |
| DOI：10.19783/j.cnki.pspc.230835 |
| 投稿时间：2023-07-02修订日期：2024-02-05 |
| 基金项目:国家自然科学基金项目资助(52107098) |
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| Station line loss rate anomaly identification and accurate tracking method of associated users based on multi-dimensional scene division |
| CHEN Guangyu1,ZHANG Shengjie1,YANG Li2,HUANG Wenhao3,NAN Yu4,ZHANG Yangfei1,HAO Sipeng1 |
| (1. School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China; 2. State Grid Fujian
Electric Power Company, Fuzhou 361004, China; 3. Sanming Power Supply Company, State Grid Fujian
Electric Power Co., Ltd., Sanming 365000, China; 4. Kaifeng Power Supply Company, State Grid
Henan Electric Power Company, Kaifeng 475000, China) |
| Abstract: |
| There is a difficulty in identifying station line loss rate anomalies and tracking associated users accurately. Thus a method of station line loss rate anomaly research and tracking associated users based on multi-dimensional scene division is proposed. First, a data preprocessing strategy based on the improved complete EEMD with adaptive noise (ICEEMDAN) algorithm is proposed. By analyzing the changing trend of the historical line loss rate curve of the station area, the extensive scene set of the station line loss rate is constructed. On this basis, the SVM clustering algorithm is used to make further quadratic partition to scene set, thus to establish multi-dimensional scene set. Secondly, an interval dynamic translation strategy based on the number of cluster cases is proposed to determine the interval range of the line loss rate standard library in different scenarios. Also an interval overlap rate strategy is used to merge the isolated intervals in the division, so as to realize the complete division of the line loss rate standard library in the station area. Finally, an abnormal line loss tracking method based on correlation analysis is given. This determines the strong correlation factors of abnormal line loss through grey correlation analysis, and quantitatively analyzes the internal correlation degree between each factor and station users based on an improved Adtributor algorithm to improve the tracking accuracy of abnormal line loss rate users. Cases are simulated and analyzed by using real data in station area. The simulation results show that the proposed method is effective and practical. |
| Key words: station grey correlation multi-dimensional scene set line loss ratio standard library accurate tracking |
