居民窃电现象的存在,不仅损害了供电企业的经济利益,也对电网安全运行构成了威胁.随着中国数字经济的快速发展以及用电采集系统的不断完善,与大数据相结合的反窃查违手段不断更新.提出了将皮尔逊相关系数、SMOTE(synthetic minority oversampling technique)算法和SVM(支持向量机)相结合的居民窃电检测方法.首先利用皮尔逊相关系数收集历史窃电用户的有效窃电数据,再利用SMOTE算法丰富有效窃电数据并生成有效窃电数据库,在此基础上,通过支持向量机训练窃电用户的识别模型,最终对窃电识别模型筛选出的疑似窃电用户进行现场实际核实,核实结果表明本文提出方法具有有效性和可行性.该方法不仅为电力企业反窃查违提供了新思路,同时也提高了工作班组的工作效率.
The exist of residents electric larceny not only damages the economic benefits of power supply enterprises, but also affects the security of electric grid. With the rapid development of China’s digital economy and the improvement of electric acquisition system, electric larceny detected methods which are based on big data are updating constantly. The paper puts forward a new method which combines Pearson correlation coefficient, SMOTE algorithm and SVM. Firstly, the paper uses Pearson correlation coefficient to collect abnormal users’ effective power stealing data which are recorded, then SMOTE algorithm is used to enrich effective power stealing database. At last the paper builds a detection mathematical model based on the data base by SVM. By comparing the results of detection mathematical model and the reality situation of users, the validity and feasibility of the method is proved. The new method not only gives a new thought for electricity anti-stealing in power enterprises, but also improves the efficient of working team.
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