Fast pbkp_rediction 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 pbkp_rediction 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 pbkp_redict 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 pbkp_rediction of loadability margins. © 2006 IEEE.

Año de publicación:

2006

Keywords:

• Oscillatory stability
• Loadability margins
• Stability limit pbkp_rediction
• Neural networks

Fuente:

scopus