Probabilistic short-circuit analysis of wind power system based on sampling with optimal density function

Abstract

Probabilistic short-circuit analysis (PSCA) determines vulnerability of the transmission systems. The failure uncertainty and fluctuating wind power add difficulty to PSCA. The pre-fault system states are derived by simultaneous solution to steady state constraints of power system and the doubly-fed induction generators (DFIGs). A hybrid probabilistic simulation is newly proposed, with the fault branches enumerated and probabilistically weighted, while the fault parameters sampled. The variance coefficient of hybrid Monte-Carlo (HMC) simulation is defined to describe the convergence, which is speeded up by the optimal HMC (OPHMC) with the density function of the fault types. The numerical analysis of IEEE RTS system shows the impacts of high-order fault and wind power by comparing expectation, variance, and distribution of the bus voltage and branch current. The accuracy, convergence, efficiency of Monte-Carlo (MC), HMC and OPHMC methods are compared.