Design And Implementation Of Electrical Information Room Environment Monitor Management System

The Electricity Information Acquisition System of the State Grid Electric can collect user load,power,voltage and other information in real time.At present,real-time monitoring of offline line loss data in the station area has been achieved,but abnormal users cannot be located.How to make full use of line loss data,play the role of line loss data,and efficiently and accurately investigate users with abnormal power consumption,this is a problem currently facing the State Grid.The rapid development of machine learning and data mining technology provides conditions for the effective use of line loss data.Applying machine learning and data mining technology to users who find abnormal electricity consumption can provide new support for the construction of power grid informatization.Combined with the current status of line loss,combined with related technologies and methods of machine learning and data mining,a comprehensive analysis system for station line loss has been designed and improved.The system can realize data processing,data analysis and data visualization.The part of data processing is the foundation of the entire system.It realizes the selection of imported data and data preprocessing,and achieves the purpose of eliminating outliers and filling missing values.Data analysis is the core function of the entire system.After the data is processed,the calculation and statistical analysis functions of the data are realized.It uses machine learning algorithms to mine user electricity data rules and provides employees with convenient information about abnormal electricity users for easy troubleshooting.The data visualization function provides a variety of results output display,such as graphics,tables and text data,making the output results more intuitive.After analyzing the requirements of the station area line loss comprehensive analysis system,this article gives the system function design,database design and Python code design.Then use Py Qt Designer to build the interface of the system,combine it with the Py Charm environment,and use the Python language to complete the functions of the entire system.The system uses the Kmeans clustering algorithm and k NN classification algorithm to analyze the user’s electricity consumption data,and obtains the address of the user’s electricity meter that caused the abnormal line loss.The research in this thesis is a meaningful attempt to combine grid data resources with machine learning in the information age to improve grid data utilization and personnel survey efficiency.The designed station area line loss comprehensive analysis system not only solves the problem of the loss of abnormal values in the electricity consumption data,but also simplifies the complicated and repetitive troubleshooting process for personnel.The operating result of the system visually displays the meter address of the abnormal user,thereby improving the efficiency of the staff in solving problems.