Diagnosis Technology Of Electricity Theft Considering The Credit Rating Of Users

The scale of power system is getting larger and larger,and the number of users is also increasing.Not all users strictly abide by the power laws and regulations.Because of the temptation of the economy,many users have the behavior of stealing electricity and not paying the electricity fee,and there are many methods and kinds of stealing electricity,which makes the power company defenseless.Therefore,it is very important for the study of user electricity index and user credit to help power companies understand users and effectively prevent electricity theft.Firstly,this paper analyzes the principle of electric energy measurement and the common methods of stealing electricity,and summarizes some common methods for stealing electricity,including under current,phase-shift,differential expansion,zero borrowing,high-power wireless interference and remote control stealing,which provide the theoretical basis for the construction of the evaluation system.Secondly,based on the concept of data mining,the paper analyzes the user’s electricity consumption index,selects the normal index and abnormal index,through which the abnormal user can be identified and analyzed whether the user is stealing electricity or leakage.Secondly,based on the concept of data mining,the paper analyzes the user’s electricity consumption index,selects the normal index and abnormal index,through which the abnormal user can be identified and analyzed whether the user is stealing electricity or leakage.Then,a credit rating evaluation index system is established,which includes the payment credit of users and the credit of electricity laws and regulations,and uses the fuzzy analytic hierarchy process to evaluate the credit rating of users.Finally,based on the analysis of electricity stealing index and the evaluation of credit rating,the deep neural network model is used to diagnose the electricity stealing of users,and it is found that considering the credit rating of users can significantly improve the identification rate of electricity stealing..Through the verification of an example,it is proved that the model and algorithm proposed in this paper can be well applied to the diagnosis of electricity theft,and the analysis considering two factors can improve the identification rate of electricity theft.By identifying the power stealing users,the economic loss of the power grid company is reduced,the social environment is purified,the research and application of anti stealing technology is promoted,the reliable operation of the power system is ensured,and the social stability is maintained.