Binary Particle Swarm Optimization Based Defensive Islanding Of Large Scale Power Systems

Power system defensive islanding is an efficient way to avoid catastrophic wide area blackouts, such as the 2003 North American Blackout. Finding defensive islands of large-scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution, if it exists, within reasonable time using analytical methods. This paper presents to utilize the computational efficiency property of binary particle swarm optimization to find some efficient splitting solutions for large-scale power systems. The solutions are optimized based on a fitness function considering the real power balance between generations and loads in islands, the relative importance of customers, and the desired number of islands. Besides providing information about the opening of transmission lines, the algorithm can also provide necessary load shedding information. Furthermore, the algorithm can provide a number of candidate solutions in order to select one satisfying the transmission system capacity constraint. Simulations with power systems of different scales demonstrate the accuracy and effectiveness of the proposed algorithm.