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

Reactive power optimization is of great importance to voltage quality guarantee, power loss minimization and thus security and economic operation of power system.Conventional optimization methods are inadequate and insufficient to optimal operation of these problems, but artificial intelligence methods are much better and more potential than the conventional methods.First of all, Considering economics of power system, minimizing of real power losses is regarded as an objective function for reactive power optimization, and CPSO and AEPSO are applied to solve it respectively. One is chaotic PSO (CPSO), chaostic disturbance and chaostic mapping are synchronously added to the PSO algorithm. The other is adaptive extended PSO (AEPSO), an average accelerating factor is added to the velocity updating formula, an extended PSO (EPSO) is first presented. Then the EPSO introduces two parameters describing the evolving state of the algorithm, the evolution speed factor and aggregation degree factor, and the control parameters are dynamically adjusted according to the evolution speed factor and aggregation factor, improve the quality of global optimal.Secondly, a multi-objective model is presented considering security and economics of power system, in which three objectives including minimizing of real power losses, minimizing the deviations of voltages from desired values and minimizing the voltage stability index. The multi-objective problem is converted into a single-objective by the multi-objective fuzzy optimization method, and SA-FA- PSO is applied to solve it. In order to avoid the search of PSO being trapped in local optimum and improve the global optimal, all three control parameters, the inertia weight and two accelerating factors, are adapted dynamically by the fuzzy rules in a fuzzy system during optimization process, and the fitness value of particles and positions are adapted dynamically through the idea of simulated annealing(SA).At last, to show the effectiveness of proposed methods, the simulation results of standard IEEE power systems are compared with PSO.