Studies On Power System Reactive Power Optimization Based On Particle Swarm Algorithm

The main task of power system control and management is to keep the grid operation under security, reliability and economy. The distribution of reactive power not only determines the voltage profile, which is an important index of voltage quality, but also affects the security and economy of power systems. Reactive power optimization is an effective measure to assure the security, improve power quality and benefit of power systems. It is obvious that the investigation in reactive power optimization of power systems is of great significance.Reactive power optimization problem in power systems is a typical non-linear programming problem with characteristics of multi-objective, uncertainty, multi-restriction and discreteness. Conventional mathematical programming techniques are inadequate and insufficient to the optimal operation of power systems due to the inherent complexity. Based on the comprehensive summarization and analysis of Particle Swarm Optimization (PSO) and fuzzy sets theory, which are applied here to reactive power optimization, systematic and thorough studies on PSO algorithm and reactive power optimization have been made and some new ideals and algorithms have been put forward. An adaptive particle swarm optimization algorithm (APSO) and a fuzzy adaptive particle swarm optimization algorithm have been proposed on the basis of heuristic rules and fuzzy sets theory. The APSOs are applied to solve reactive power optimization problem. A multi-objective model is presented considering security and economy of power system and a cooperative co-evolutionary PSO algorithm is designed according to cooperative co-evolutionary approach for global optimal reactive power and voltage control of large-scale power systems.After the thorough research of stochastic optimization mechanism and the control parameters of PSO, two new algorithms, called adaptive particle swarm optimization and fuzzy adaptive particle swarm optimization (FAPSO), are presented. According to the relationship between the control parameters and the fitness value of particles, APSO adjusts parameters adaptively in optimization process to find the global optimum based on some heuristic rules. APSO possesses stronger capability of escaping local optimal solution and finding global optimization as new particles are added. In the FAPSO, all three parameters, the inertia weight ω and the two learning factors c₁ and c₂, are...