Power System Controlled Islanding Using Modified Discrete Optimization Techniques

Controlled islanding is implemented to save the power system from experiencing blackouts during severe sequence line tripping. The power system is partitioned into several stand-alone islands by removing the optimal transmission line during controlled islanding execution. Since selecting the optimal transmission lines to be removed (cutsets) is important in this action, a good technique is required in order to determine the optimal islanding solution (lines to be removed). Thus, this paper developed two techniques, namely Modified Discrete Evolutionary Programming (MDEP) and Modified Discrete Particle Swarm Optimization (MDPSO) to determine the optimal islanding solution for controlled islanding implementation. The best technique among these two which is based on their capability of producing the optimal islanding solution with minimal objective function (minimal power flow disruption) will be selected to implement the controlled islanding. The performance of these techniques is evaluated through case studies using the IEEE 118-bus test system. The results show that the MDEP technique produces the best optimal islanding solution compared to the MDPSO and other previously published techniques

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