Research And Development Of Load Pattern Extraction And Recognition System Using Qt

Various advanced metering devices, such as sensors, intelligent instruments, are being installed in the distribution network, and each monitoring point load curve is made up by the energy consumption data collected. The typical load pattern and load characteristics of power users can be obtained through cluster analysis, which is of great significance to improve the reliability of power system operation, improve the efficiency of power grid assets and save energy. The load pattern extraction and recognition has become a hot topic at domestic and foreign.In this thesis, problems related to the load pattern extraction and recognition were studied, and the extraction and recognition system for electricity consumer was designed and implemented. Main work is as follows:(1) Normalization methods and clustering algorithms used in the process of load pattern extraction were introduced, the usual clustering validity indexs were summarized,and the time series similarity measure methods adopted in the process of load pattern extraction and recognition were introduced.(2) The effect of various factors in the steps of clustering on clustering results was studied. Mainly from the influence of the normalization methods on the clustering results,the dependence of clustering results on data set, the algorithm stability and the sensitivity of clustering algorithm to the input order of data to analyze k-means, FCM, SOM,hierarchical clustering and spectral clustering, and give their match relations.(3) The method of fast extracting load pattern was studied. A fast extracting load pattern method using the random sampling and the segmentation was proposed and the validity was verified by experiments.(4) Load pattern extraction and recognition system was designed, the function and application of this system were analyzed, and the detailed process of load pattern extraction and recognition was presented. The whole system design was realized,including database design and functional design. On this basis, the system was developed using Qt. The system was demonstrated by the application of load pattern extraction and recognition in detection of electricity behavior irregularities.