Processing And Analysis Of Electic Load Data In Power Systems

Load forecasting is one of the most important issues in power system planning, dispatching andmarket operation, and it becomes more important with the development of the smart-grid and plug-inhybrid electrical vehic le (PHEV). Therefore, it is of great significance to improve the accuracy of loadforecasting.Load forecasting is heavily dependent on historial data and related factors. It is the objective of thisthesis to do systematical research as as to improve the accuracy of load forecasting.On the one hand, as to the historical load data, there are usually some abnormal data in the electricload database derived from the SCADA system, and the accuracy of load forecasting could then beimpacted. Hence, it is necessary to identify and then correct the abnormal data before the load data areused for load forecasting or power system analysis. However, so far the existing research work in thisarea is mainly done in one dimension. Given this background, considering both horizontal and verticalcontinuities of electric loads, firstly, a density evaluation based method is presented to identify and thencorrect abnormal data in two dimensions. Then, a new method for load de-noising is presented based onthe two-dimension wavelet threshold de-noising. Specifically, the load data is transformed into a matrixof gray-scale images by normalization. The images are processed by employing the two-dimensionwavelet threshold de-noising method. Finally, the de-noised data are obtained after de-normalization.The feasibility and efficiency of the developed method are demonstrated by the improvement of loadforecasting accuracy.On the other hand, as to the analyzed material of related factor, an effort is mainly made to calculateand analyze the annual maximum high-temperature related loads by employing the maximum load-basedcomparison method and the base load-based comparison method respectively, to do the study of thecorrelation analysis and sensitivity analysis between meteorological load and temperature by dividing theload into meteorological load and economic load, and to calculate the load composition of the wholepower system by employing the statistical inference method based on the monthly electricityconsumption data as well as the load data of each kind of industry loads collected by the load control system. And the result shows that these works could provide efficient decision-making assistance forpower system planning, dispatching, market operation, as well as accurate load forecasting.