Research And Application Of Intelligent Analysis Method Of Line Loss Anomaly Based On Data Drive

Abnormal line loss management is an important factor that restricts the development of power network.Solving this problem is helpful to reduce loss and save energy in power distribution area,which has important guiding significance for power network planning and construction.In recent years,with the rapid development of advanced technologies such as big data and artificial intelligence,traditional power grids have been transformed into smart grids.Smart electricity meters and electricity acquisition systems for power users have been gradually popularized in power supply enterprises.Conventional line loss management methods,which mainly focus on manual screening,cannot effectively reflect the relationship between power data.It restricts the real time and accuracy of line loss analysis and abnormal diagnosis of line loss and seriously restricts the efficiency of line loss management.Based on this,it is an inevitable trend for the development of line loss anomaly management methods to combine big data with deep learning theory and solve the problems related to line loss anomalies in a data-driven way.The main work of this paper is as follows:(1)In view of the lack of statistical line loss rate data in the collected statistical table of stations,which makes it impossible to determine whether the station is abnormal or not,the prediction filling method of statistical line loss rate of stations based on DNN and the prediction filling method of statistical line loss rate of stations based on LSTM time series algorithm are proposed.It realizes the effective prediction of the missing data of statistical line loss rate in station area.(2)Aiming at the problem that the assessment threshold of statistical line loss rate determined by abnormal stations with line loss is set too widely,and the assessment threshold of statistical line loss rate is set the same for all stations,and there is lack of refinement management,a refinement threshold setting method based on theoretical line loss rate of stations is proposed.First of all,aiming at the calculation difficulty of theoretical line loss rate,a calculation method of theoretical line loss rate based on 1D-CNN is adopted to calculate theoretical line loss rate of platform area conveniently and effectively.Secondly,the paper classifies the stations according to the theoretical line loss rate,and sets a reasonable statistical line loss rate assessment threshold for each type of stations.(3)In order to solve the problem of abnormal line loss,the cause of abnormal line loss is traced.Firstly,the abnormal line loss caused by power theft is analyzed,and the detection algorithm of power theft by power users based on Densenet-RF is proposed.Secondly,the causes of other abnormal line loss are analyzed,and the corresponding detection method of abnormal line loss is proposed.Finally,the whole line loss anomaly analysis process is formed.(4)According to the functional requirements,the intelligent diagnosis system for the abnormal line loss of the platform area is developed.Based on C/S framework,the software function modules,such as data storage and management,line loss anomaly platform query,line loss anomaly diagnosis and analysis,missing data filling,closed-loop management,etc.,are completed,which realize the effective analysis and scientific diagnosis of line loss anomaly,and the refined management of line loss in the station area,and improve the efficiency of line loss management.In conclusion,starting from the relevance and dynamic nature of data,this paper makes an in-depth analysis of line loss data with technical means,builds a multi-directional intelligent analysis system for line loss anomalies,and realizes the scientific diagnosis of line loss anomalies in the form of software application,which improves the management level of line loss of power supply enterprises and the operating efficiency of power grid.It is conducive to promoting the construction of a safe,reliable,clean and environment-friendly,open and compatible modern and intelligent power grid.