Study On Load Modeling Based On Online Power Quality Monitoring Data

The accurate electric load model is the basis of power flow calculation and stability analysis. Recently, installations of the power quality monitoring devices in substations provides a new way for load modeling. Comparing with the specialled designed load transient recorders, the widely installed, low-cost and multi-functional power quality monitoring system will make the promotion of load modeling more convenient with lower investment.To overcome the shortcomings of basic genetic algorithm for load modeling, proportional selection strategy and linear self-adaptive mutation strategy are designed. The improved GA is used to identify composite load model parameters based on measurement-based load modeling. Practical modeling shows that the proposed genetic algorithm is effective in overcoming precocity, accelerating convergence and improving the stability of parameters. Based on above achievements, the user-friendly software for load modeling is developed, which provides an effective way to establish load model. Based on the sensitivity analysis of composite load model parameters, this paper proposed a new identification strategy, in which the majority of parameters are fixed and only the parameters with higher sensitivity are identified. Case study shows that the stability of the parameters identified by the proposed strategy is greatly improved.With the increasing of penatration of distributed generation'in load aera, the impact of distributed generation on load modeling of distribution network should be studied. Based on the analysis of the performance of DFIG that is connected to distribution network by simulation, pointed out that the power characteristics of DFIG is close to the static load when the grid voltage dips slightly(within 30%), the normal composite load model can aslo be used to describe the load characteristics of distribution network with DFIG integration. The idea is testified by modeling of distibutin network under three different capacity of wind power generation.