Study On The Application Of Particle Swarm Optimization (PSO)Algorithm In Power System Reactive Power Optimization And Economic Load Dispatch

Energy is a necessary condition for the development of human society. In the modern society, electricity is one of the most important secondary energy, which has a very wide range of applications in the national economy and people’s daily life. With the development of electricity technology, modern power industry has entered the developing period of large-scale system, extra high voltage, long-distance, high-capacity, but various problems in power system operation have also become increasingly prominent, which has an great impact on economy, safe and stable operation of power system. Reactive power optimization and economic load dispatch is typical of two optimization problems. In this paper, which two problems are research by particle swarm optimization algorithm and its improved.In reactive power optimization problem, the mathematical model of multi-objective optimization has been established in this paper. The objectives consist of real power loss and voltage quality. This paper uses fixed-weight method to transform the multi-objective functions to the single objective function. Three optimization algorithms are used:they are particle swarm optimization (PSO), adaptive weight particle swarm optimization (AWPSO) and inheritance learning particle swarm optimization (ILPSO), which has been used to solve the IEEE30, IEEE118bus system.The results show that ILPSO algorithm can effectively solve the reactive power optimization problem. Meanwhile, the different species and iterations of algorithm which are selected to calculate and analyze reactive power optimization problem. The results show that different parameters of the algorithm have a great impact on the results of optimization.In power system economic load dispatch problem, the optimization model of total generation minimum cost has been established in this paper. Also the system transmission loss has been considered and the penalty function has been considered to handle the power balance constraints. The three optimization algorithms are used:they are particle swarm optimization (PSO), adaptive weight particle swarm optimization (AWPSO), inheritance learning particle swarm optimization (ILPSO). Three simulated examples including3-generators,6-generators and15-generators are calculated by the three optimization algorithms. The calculated results are compared with literature results; ILPSO algorithm is an effective and feasible method to solve the power system economic load dispatch problem. The impact of different algorithm parameters to optimize on the results is analyzed. The influence of different algorithm parameters on the optimization results is analyzed.The results show that selecting the correct parameters can get suitable optimization results. For40-generators, the valve point effect is considered, and the calculation results with ILPSO are better than the literature results.In this paper, reactive power optimization and economic load dispatch are studied by three kinds of particle swarm optimization of the different parameters.It can be concluded to select the correct algorithm parameter, which has a great influence on optimization results. And ILPSO algorithm is an effective and superior algorithm for solving these two problems.