A Study On Power Load Modeling And Reactive Power Optimization Algorithm Incorporating Load Model

This dissertation studied the following issues: power load modeling based on support vector machine, selection of kernel function in model structure, optimization of model parameters by using Bayesian evidence framework, access to accurate data and reactive power optimization algorithm considering load model. The study solved the problem of establishing model for complex power load. Traditional load model is inflexible and inaccurate, and didn't have good generalization ability. All these problems had been solved in this dissertation. It also studied the synthetic reactive power optimization considering load model. The main contents of the dissertation are as follows:The accuracy of load model and its parameters have great effect on power system digital simulation results. This dissertation presented a support vector machine (SVM) load modeling method by using Bayesian evidence framework. This method regarded load bus as"black box", and applied SVM regression theory to establish non-mechanism load model. Gaussian radial basis kernel function was used. It adopted three inference levels of Bayesian evidence framework to optimize the model parameters. This method could change model structure flexibly, also could identify and optimize model parameters. It established a non-mechanism load model that can reflect load characteristics.The wide area measurement system (WAMS) ,which is mainly characterized by synchronous measurement and high-speed communication with high-precision and high-density among differernt places, could provide real-timely massive dynamic data that reflect the system characteristics, therefore, this dissertation proposed using the SVM and information acquired WAMS to establish load model. Simulation results showed that this model has less identification parameters. It owned rapid calculation speed and good generalization ability, so it could describe the actual load characteristics more accurately. As for large disturbances, this dissertation used SVM and information of power fault recording and monitoring system (PFRMS) to establish load model. The established PFRMS could meet the needs of providing accurate data. Utilizing the recorded fault data to replay the transient process, the dynamic load model could be verified by the comparison between measured curves and simulation ones.Considering that load quantity and its chang trend produce certain influence to reactive power optimization, this dissertation presented a reactive power optimization which aimed at minimizing network loss during the whole electric power dispatching period,improving voltage quality and reducing action number of control devices. Taking load changes into account,particle swarm algorithm(PSO)was combined with simulated annealing algorithm(SA)to optimize reactive power synthetically. Simulation results for several testing systems demonstrated that this cooperative algorithm was simple and can be easily realized with high efficiency.