Application Of Importance Sampling Method In Probabilistic Load Flow Of Power System

Power system security analysis is an important part of power system operation and control. For a long time power system stability analysis has been based on the uncertainty of the trend calculated. However, the system load changes, equipment investment of time, Network topology uncertain factors such as the bus load, the power system stability analysis if the uncertainty flow calculation method, on the request of a large number of possible scenarios for a large number of programs to calculate the time, it is hard to bear and the overall system is very difficult to reflect the situation.In Monte-Carlo sampling technology normally used in power system, probabilistic simulation has low efficiency and needs large sample size. This paper presents importance sampling method for probabilistic load flow of power system. Generate samples of load model according Monte-Carlo sampling method, then using the kernel sampling density as the importance sampling density, carry out importance sampling simulation to compute the cumulative distribution function(CDF) of transmission line flows. Numerical results of IEEE39-bus shows that comparing with Monte-Carlo simulation, importance sampling method can evidently reduce the sampling variance and accelerate the convergence speed of iteration calculation.