Fast prediction of loadability margins using neural networks to approximate security boundaries of power systems

• Published in: IET electric power applications Vol. 1; no. 3; pp. 466 - 475
• Main Authors: GU, X, CANIZARES, C. A
• Format: Journal Article
• Language: English
• Published: Stevenage Institution of engineering and technology 2007; The Institution of Engineering & Technology
• Subjects: Applied sciences; Approximation; Benchmarking; Boundaries; Computer simulation; Deregulation; Disturbances. Regulation. Protection; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Miscellaneous; Neural networks; On-line systems; Operation. Load control. Reliability; Power networks and lines; Security; Machine testing; Electric generator; Deregulation; Output power; Steady state stability; Power system economics; Power system stability; Iterative method; Neural network; Power system security; Search algorithm; Learning; Backpropagation algorithm; Electrical network; Stability limit; Voltage stability; System simulation; Contingency table; IEEE standards; Safety; Train; Comparative study; Dynamic load
• ISSN: 1751-8687; 1751-8660; 1751-8695; 1751-8679
• DOI: 10.1049/iet-gtd:20060265

Determining loadability margins to various security limits is of great importance for the secure operation of a power system, especially in the current deregulated environment. In this article, a novel approach is proposed for fast prediction of loadability margins of power systems based on neural networks. Static security boundaries, comprised of static voltage stability limits, oscillatory stability limits and other operating limits such as generator power output limits, are constructed by means of loading the power system until these security limits are reached from a base operating point along various loading directions. Back-propagation neural networks for different contingencies are trained to approximate the security boundaries. A search algorithm is then employed to predict the loadability margins from any stable operating points along arbitrary loading directions through an iterative technique based on the trained neural networks. The simulation results for the IEEE two-area benchmark system and the IEEE 50-machine test system demonstrate the effectiveness of the proposed method for on-line prediction of loadability margins.