To protect the security of electricity consumption in low voltage substation,a feature mining-based identification method of electric stealing behaveior in low voltage substation is proposed.Firstly,the ZigBee network model is used to collect the power consumption data of the users in the low voltage substation,and extract the abnormal power consumption features of the users such as the indicators of sudden change in power consumption,the indicators of difference in power consumption data,and the indicators of change in power consumption,etc.Then,the extracted features are subjected to uncertainty measurement,correction and normalization to obtain representative features.Then,the principal component analysis method is used to reduce the dimensionality of the features to improve the efficiency of the classifier.Finally,the dimensionality reduced features are used as the input information of the support vector machine classifier and are matched with the three types of indicators to determine whether there is electric stealing behavior.The experimental results show that the method can effectively identify electric stealing behavior in low-voltage substation,and the identification accuracy rate reaches 93.4%.The method can provide effective technical support for preventing and combating electric stealing behavior.
维普
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