Damping controller design for power system oscillations using hybrid GA-SQP

In this paper, a hybrid optimization method, GA-SQP, is described in which the genetic algorithm (GA) is a stochastic method is combined with the sequential quadratic programming (SQP) method, which is a deterministic method. The power system stabilizers (PSSs) parameters tuning problem is converted to an optimization problem which is solved by hybrid GA- SQP optimization algorithm. It was shown that although the SQP is fast, it is not able to solve this problem properly and is very sensitive to the choice of initial point. The GA was able to solve the problem after a large number of generations. It was shown that the proposed method is able to determine the final solution considerably faster than the GA while it is not sensitive to the initial point. GA is the main optimizer of the algorithm, whereas SQP is used to fine tune the results of GA to increase confidence in the solution. The New England 10-unit 39-bus standard power system, under various operation conditions, is employed to illustrate the performance of the proposed method. The results are very encouraging and suggest that the hybrid GA-SQP algorithm is very efficient in damping improvement of the power system.