Multi-objective Power System Reactive Power Optimization Based On Adaptive Particle Swarm Algorithm

Power quality has got much attention, it linkes reactive power closely. So reactive power optimization should take the voltage quality into account. The traditional methods of reactive power optimization, neglected multi-objectives in reactive power optimization, they only focused on the power loss and overlooked the importance of voltage quality and other objects. In practical problem users need to meet various environmental diversity choice. Power loss the characterization of the power economic and voltage deviation the characterization of the power quality aways compete with each other. Clarifying the relationship between the two objects is avail to understand system features and provide users guidance. Some researchers considered multiple objectives of the power system, but they most adopt various forms to aggregate multilpe objects to a single one. They have limitations to deal with these issues. Ususally aggregating weight method select a weight target with blindness, and it can not accurately reflect the relationship between the various objectives, and can not adapt to the need of users who want a variety of diverse needs.In this paper, for the reactive power optimization with objects power loss and voltage deviation, a new adaptive multi-objective particle swarm optimization (AMOPSO) is proposed. This algorithm adopt improved NSGA-II non-dominated sorting strategy, and use dynamic weighting method to choose the optimal particle population, to increase the diversity; dynamic inertia weight and mutation strategy are used to overcome the local optimum and improve the global searching capability. The test functions ZDT1 ~ ZDT4 verify the performance of the algorithm. Exhibit the excellent performance of this algorithm with comparisons. The comparisons between diffrent strategies shows the excellent performance of the improvement strategies used in this article. For constrains of multi-objective optimization, we proposed a preference strategy and a preference factor. Preference strategy will not destroy the diversity of optimal solutions, when it provide the users more flexible solutions to choose, and the preference factor will help clarify the relationship between properties and systems, to provide users more guidance.The adaptive multi-objective particle swarm optimization for reactive power optimization, it improved the constraints and the initialization processing, using a priori knowledge of the initialization strategy and an adaptive penalty function strategy. IEEE30,IEEE14 bus system are used to demonstrate the feasibility of the method and the effect, experiments show that AMOPSO used in reactive power optimization can bring users excellent solution. The comparison with NSGA-II shows AMOPSO's excellent performance, especially in the convergence rate. Analysis of improvement strategies'performance, confirm the effectiveness of improvement strategies. Experiments show that the voltage preference strategy can not destroy the diversity choice of the electricity sector, while providing diverse options for the special users. Voltage preference factor, can quantify the special needs of users, and help providing services.