Research On Power System Operation Evaluation And Optimization Based On Power Grid Operation Data Set

With the continuous development of the smart grid,the level of information in the power industry continues to improve,and the intelligent components and devices have been applied to the power system,the power grid automation platform,accumulated and processed a large amount of big data of power grid.Big data of power grid not only has the basic characteristics of big data,with a large volume,diversity,low density and fast characteristics of the value,in addition to the big data of power grid with obvious timing characteristics.The rapid growth of big data of power grid brings great challenges to the measurement,optimization and scheduling of power production.The traditional method of data processing is limited to data processing ability,mainly based on the sampling of the overall information,according to the sampling data to determine the typical operation modes of power grid,the typical operation modes of power grid analysis results were extrapolated to obtain the grid long-term operation behavior.The processing methods of big data,can be directly with the data as the research object,by using the method of analyzing the data driven directly available information from the data,power grid operation mode characterization and optimization of power grid operation.This paper presents a method of data preprocessing for big data of power system,by using the redundant characteristics of large data and the physical relation between the electrical quantities,the defect data is patched to improve the utilization efficiency of big data of power system.By screening big data of power system,according to the research objectives,select the key information of grid operation,the power grid operation data set can be established,which provides a data base for the analysis of power grid based on operational data.In this paper,a new method of partial priority clustering is proposed,which is used to distinguish the key attributes.On this basis,the typical operation mode of power grid is obtained,which provides an effective tool for the research of power network optimization.Extract the key attribute data of power big data sets each time,establish the data set of the corresponding power grid operation mode,and cluster the data set by clustering algorithm and clustering-fusion algorithm,the typical data and probability of occurrence of typical operation modes of power grid acan be obtained.In this paper,the active power network loss assessment model based on the power grid operation data set is established.On this basis,this paper presents adjustment coefficient of the generator excitation system calculation model,analyzed the difference coefficient under the influence of reactive power scheme generator voltage regulation of power grid,the adjustment coefficient of the generator excitation system optimization tuning method based on,in order to improve the voltage level of power grid,reducing active power loss.After this method is adopted,the uncertainty of power generation and load can be fully considered while power grid optimization,and the optimized result is more suitable for the actual operation of power grid.