A novel reactive power optimization solution using improved chaos PSO based on multi-agent architecture

Reactive power optimization plays an important role in safe and economic operation of power systems. However, with multiple mixed variables, this issue is well known as complex, nonlinear and multi-constrained. In this paper, combined particle swarm optimization (PSO) algorithm with chaos and multi-agent system, a novel algorithm (MACPSO) is developed and applied to reactive power optimization of power system. It synthesizes advantages of the swarm search of PSO and the intelligent search of agents. Seen as a particle of PSO during the search process, each agent competes and cooperates with the neighboring agents so as to obtain solutions of high quality swiftly. To strike a balance between intensification and diversification strategy and to reduce the probability of falling into local optimum, chaos optimization algorithm is adopted in the proposed algorithm. In order to verify the effectiveness of the proposed algorithm, the performance of MACPSO is evaluated on four classical testing functions and reactive power optimization. Experiment results show that MACPSO has excellent search ability and highly accurate convergence. Copyright (c) 2013 John Wiley & Sons, Ltd.