Reactive Power Optimization In Power System Based On Cloud Model And Particle Swarm Optimization

The reactive power optimization in power system refers to considering the parametersand loads of the network structure, makes the full use of the power system reactive power,improves the voltage quality and reduces network losses by adjusting the control variablessuch as generator’s terminal voltage, OLTC’s (On-Load Tap Changer) transformer ratio andreactive power compensation device’s stalls. The traditional reactive power optimizationalgorithms have significant limitations and they are improper in dealing with the discretevariables. For reactive power optimization features complex in recent years, people began touse artificial intelligence algorithms to solve the optimization problem of reactive power,outstanding nature of the use of intelligent algorithms can better solve the discrete variables.Among them, the development time is not long PSO with fewer parameters converge quicklyrealized the advantages of simplicity, is widely applied to the reactive power optimizationproblem, but it still exists in the particle swarm algorithm is easy to fall into local optimalvalue and Disadvantages late slow convergence, pending further optimization.For random PSO initial solution, we use the chaotic particle swarm optimization strategyto initiate operation, in order to increase the value of the diversity of particles and givesimproved chaotic particle swarm optimization（CPSO）. And mathematical models for solvingoptimization problems and related algorithms have been studied from a system runningeconomic considerations, the minimum active power loss as the objective function, theestablishment of a single objective reactive power optimization model. Improved algorithmsfor solving them get higher quality solutions.Particle swarm algorithm easy to fall into local optimal value and the late slowconvergence problem of particle swarm optimization to further improve the ability to explorethe solution space, from the perspective of optimization mechanisms designed to start twoimproved strategies: Are based on normal cloud generator cloud model evolution strategy andvariation strategy. Evolutionary strategy is based on the fitness value of particles into theparticle population near the optimal value, far closer to the optimal value and the optimalvalue of the three subgroups and were taken to different inertia weight generation strategy forprocessing, which is closer to inertia subgroups dynamic adaptive optimal particle weightadjusted by the normal cloud generator, get rid of the shackles of the algorithm into a localoptimum value. and variation strategy in the late iterating through normal cloud mutationoperator to achieve the particles to accelerate post-convergence. Through simulation analyzesthe feasibility of the two strategies, and reactive power optimization applications demonstratethe effectiveness of the algorithm.This paper considering the system operation safety and economy, with a minimum activepower loss, voltage deviation minimum and maximum static voltage stability margin as theobjective function, a fuzzy multi-objective optimization model of reactive power. On the basisof the above chaotic particle swarm algorithm, combined with normal cloud generator based on evolutionary strategies and mutation strategy, given hybrid optimization algorithm-CloudAdaptive Variation Chaos Particle Swarm Optimization（CAVCPSO）. this paper usingMATLAB7.0programming and testing of systems and IEEE118standard IEEE30node testnode system simulation. By comparing the experimental results can be verified CAVCPSOoptimization algorithm to avoid falling into local optimum and global convergence andachieved effective results.