Self-adaptive Differential Evolution Algorithm for Reactive Power Optimization

A novel algorithm, self-adaptive differential evolution algorithm (SaDE), is introduced to deal with reactive power optimization problem. The optimization goal is to minimize the active power losses while maintaining available voltage profiles. In SaDE, the selection of learning strategies and the two control parameters, F and CR, are gradually self-adapted according to the learning experience. Self-adaptive differential evolution algorithm is successfully applied in IEEE-30 bus power system. The reactive power optimization results show that SaDE can achieve a better solution than other two algorithms and the saving percent of system losses are obviously higher by SaDE, comparing with comprehensive learning particle swarm optimizer (CLPSO) and differential evolution algorithm (DE).