基于改进SVM的电力用户异常用电行为检测方法研究

Research on detection method of abnormal power consumption behavior of power users based on improved SVM

针对现有异常用电行为检测方法提取特征单一、检测精度不高等问题,文章提出了一种将改进蚁狮优化算法和改进支持向量机相结合,用于检测电力用户异常用电行为。采用决策树优化支持向量机转换为多级分类器,通过改进蚁狮优化算法优化支持向量机参数,提高训练速度。通过试验对多种异常用电行为进行分析,验证了所提方法的优越性。结果表明,与传统的异常数据检测方法相比,所述方法具有更高的检测精度和更低的训练时间。

Aiming at the problems of single feature extraction and low detection accuracy of the existing abnormal power consumption behavior detection methods,an improved ant lion optimization algorithm combined with improved support vector machine(SVM)are proposed to detect the abnormal power consumption behavior of power users.The decision tree is used to optimize the SVM into a multi-level classifier,and the ant lion optimization algorithm is improved to optimize the parameters of the SVM to improve the training speed.A variety of abnormal power consumption behaviors are analyzed through experiments to verify the superiority of the proposed method.The results show that the proposed method has higher detection accuracy and lower training time than traditional anomaly data detection methods.