SMALL SIGNAL STABILITY ANALYSIS AND NEURAL NETWORK ASSESSMENT OF POWER SYSTEMS WITH LARGE-SCALE WIND FARM

Abstract

With the increasing penetration of wind power on power systems, the wind farms models are required to be developed for representing the steady behaviour of wind farms on power systems. The impact of large-scale wind power on power system small signal stability is investigated in this paper. The wind generator model is established, which its shaft is taken into account, and then the power flow calculation of power system with wind farm integration is proposed. The analysis of oscillation modes, damp ratios and participation factors are performed by the solution of the eigenvalues when operation parameters, such as the wind power capability and the distance to integration, change. A two-area system is employed to analyse for drawing some important conclusions. Based on these analyses, generalized regression neural network (GRNN) is applied to carry out small signal stability forecast. The feasibility and validity of the GRNN forecast method is compared with traditional eigenvalue analysis.