Identification and improvement of probabilistic voltage instability modes of power system with wind power integration

Abstract

A probabilistic-based approach is presented to identify and improve small-disturbance voltage stability of power systems incorporating a wind farm, taking into consideration the stochastic uncertainty of system loads, output of wind turbine generators and synchronous generators. The whole system model is based on plug-in modeling technology (PMT) with detailed representation of doubly-fed induction generator (DFIG) dynamic model. The probabilistic distribution of state-matrix eigenvalues is obtained for analyzing the small-disturbance stability of power systems. Voltage stability correlation ratio and voltage instability mode coefficient are proposed for identifying voltage modes and improving voltage stability. The proposed approach is tested on a three-machine system and a nine-machine system. The simulation results show that the approach can quantify the well-known observation that wind farm integration may increase the voltage instability of the power system. The stochastic variations can induce a higher probability of system instability when compared with the one that does not have wind generation. Besides, with wider eigenvalues distributing after the wind generation installation, it makes more difficult for stability improvement. In this paper, static VAR compensator (SVC) is installed at the weakest point which is determined by the voltage instability mode coefficient to improve the system probabilistic voltage stability. The final results validate the efficiency and feasibility of the proposed approach.