Multi-Objective Reactive Power Optimization Based On Adaptive Chaos Particle Swarm Optimization Algorithm

The control of reactive power optimization in electric power system can not only reduce power loss,but also improve voltage quality. Consequently it is of great importance to security and economic operation of power system.According to the requirements of power system operation, reactive power optimization problem can be divided into single-objective reactive power optimization and multi-objective one. To meet the needs of economic and safe operation of power system, this paper establishes the minimum set of system power loss, the minimum node voltage offset and the largest static voltage stability margin in the multi-objective reactive power optimization model.The Particle Swarm Optimization Algorithm is mainly studied in the paper. And the composition of particle swarm optimization algorithm and the optimization principle were analyzed deeply. In particle swarm reactive power optimization it is easily falls into the local optimal solution in the iterative process of optimization due to the particles randomly generated on behalf of variable values and the slow convergence problem finally. Consequently, in this paper the chaotic particle swarm algorithm produced by the chaotic optimization algorithm integrated into the particle swarm algorithm is proposed to solve the problem of multi-objective reactive power optimization. The chaotic method is adopted to increase the diversity of control variable value in the initialization of the algorithm, that the value of reactive power optimization of control variables. The particle swarm algorithm is used to calculate the fitness of each particle that corresponds to reactive power optimization of the objective function value and merit-based selection in accordance with its size control variable values of chaos optimization to help optimize the reactive power control variables out of local optimum area. In accordance with the objective function value of reactive power optimization, it is adaptively adjusted its coefficient of inertia weight to enhance the global and local search capabilities.The chaotic particle swarm algorithm is applied to reactive power optimization,through MATLAB programming right IEEE14-bus and IEEE30-bus system reactive power optimization calculation,and with elementary particle swarm optimization and genetic algorithm comparison, the results show that the algorithm proposed in this paper effectively reduces the system power loss and improves system voltage quality level,and has a better ability of global optimization and faster convergence speed.