Fast Prediction of Loadability Margins by Constructing a Small-Signal Stability Boundary Based on Neural Networks

Abstract

Determining loadability margins to various security limits is of great importance for the secure operation of a power system. A novel approach is proposed in this paper for fast prediction of loadability margins with respect to small-signal stability based on neural networks. Small-signal stability boundaries are constructed by means of loading the power system until the stability limits are reached from a base operating point along various loading directions. Back-propagation neural networks (BPNN) for different contingencies are trained to approximate these stability boundaries. A search algorithm is then proposed to predict the loadability margins from any stable operating point along arbitrary loading directions through an iterative technique based on the trained BPNNs. The simulation results for the IEEE two-area benchmark system demonstrate the effectiveness of the proposed method for on-line prediction of loadability margins.