Optimal Power Flow Based on Genetic Algorithms and Clustering Techniques

Optimal power flow problems have been studied extensively for the past decades. Two approaches for solving the problem have been distinguished: mathematical programming and evolutionary algorithms. The first is fast but is not converging to a global optimum for every case. The second ones are robust but time-consuming. This paper proposes a method that combines both approaches to eliminate their flaws and take advantage of their benefits. The method uses properties of genetic algorithms to group their chromosomes around optima in the search space. The centers of these groups are identified by clustering techniques and furthermore used as initial points for gradient based search methods. At the end, the proposed method finds global optimum and its closest local optima. Continuous Newton-Raphson method is used to overcome ill-conditioned points in search space when calculating power flows. The proposed method is compared against similar methods showing considerable improvement.