Reactive Power Optimization Of Power System With Wind Farm Based On Particle Swarm Optimization

Today, the economy of our country develop rapidly, and the demand of electricity becomes more and more. People pay more attention to the security and stability problem of the power system. The quality of the voltage is one of the most important factor which will affect the power system. Then the distribution of the reactive power determines the quality of the voltage, which is an important index of voltage quality. Through the reasonable reactive power, we can not only ensure the security and stability running of the power system, but also reduce the active power loss of the power system.Through researching and analyzing the basic Particle Swarm Optimization and the improved Particle Swarm Optimization. In this paper, we propose an improved algorithm. Firstly, chaotic sequence was utilized to pretreat the positions of particles to make them have ergodicity, and then the reverse learning ability of the worst individual particle was introduced into the velocity update iteration of the particle. Simulation results show that the improved algorithm significantly improves the premature convergence of particle swarm algorithm.Calculation of reactive power optimization is thought as a very complicated nonlinear programming problem, its constraint equation is a high order equations which is called power flow equation. In this equation, it contains lots of variables and constraint condition. The variables include continuous variables and discrete variables. Through adjusting the voltage of generator, the on-load tap changer and the optimal capacity of the capacitor, we can achieve the goal of reactive power optimization. So we can reduce the loss of the power system and the probability of the voltage exceeding, making the power system run securely and stably.We introduce the basic knowledge of wind power and select the wind farm which has doubly fed induction generator, then the improved Particle Swarm Optimization algorithm which is proposed in the second section is applied to the calculation of reactive power optimization with wind power generators. The paper introduces some problems on the application, mainly including the time-interval strategy of the wind and the encoding of the control variables. We select the loss of active power minium as the objective function which also contains a penalty function for a model. We adopt P-Q decomposition method for power flow equation calculation. At last, this paper gives the calculation steps of the reactive power optimization with wind power generators. To verify the effectiveness of the proposed algorithm, we adopt the IEEE-57 node standard test system and the wind farm. The improved algorithm was applied to this system. Compare with the CPSO(Chaos Particle Swarm Optimization), GPSO(Grouping Particle Swarm Optimization), LH-DMPSO(Lowdiversity Highdiversity-Dual Mutation Particle Swarm Optimization), CBMPSO(Cross-factor and Bilingual learning Mechanism Particle Swarm Optimization), we can draw the conclusion that the improved Particle Swarm Optimization algorithm reduce the loss of active power effectively.