Preventive and corrective control applications in power systems via Big Bang-Big Crunch optimization

This paper proposes a new method of optimization, the Big Bang-Big Crunch (BB-BC) method, for the optimization of preventive and corrective control actions to enhance the dynamic security of a power system against transient instabilities. The control actions, generation rescheduling and load shedding, are considered as constrained optimization problems with static and dynamic security constraints. These optimization problems are solved through the BB-BC method to minimize some operational costs related to the control actions. To reduce the size of the search spaces and the computational burden, decision trees and correlation coefficients are used as feature selection tools, which determine the most effective generators and loads for shaping the system's transient stability. The proposed method is applied to a test system and compared with genetic algorithms, particle swarm optimization, differential evolution and active set method. The BB-BC method is promising since it gives comparable results with the other population based optimization methods.