Reactive Power Optimization Based On Improved Particle Swarm Optimization Algorithm

In recent years, with the rapid development of economy, the scale of the grid is graduallyincreasing, the demand of residents for electricity becomes more and more and people pay moreattention on the secure and economical running of the power system. The reactive poweroptimization is reducing system losses and voltage-limit on the maximum range, supplyingelectricity to consumers safely and economically by adjusting the generator terminal voltage,adjusting transformer tap position and switching reactive compensation equipment according tothe system network load and the trend. The reactive power optimization has very importantpractical significance in improving the voltage quality and power system stability, reducingsystem losses and increase economic benefits.A new hybrid particle swarm algorithm is proposed through studying the PSO algorithm andreactive power optimization method in-depth in this paper. The algorithm combines differentialevolution and simulated annealing with PSO and tracks the third value producing by the differenceinformation of particle besides tracking individual extreme value and the global extreme value.When the speed of the particles is less than a given value in a given dimension, it will reinitializethe particle velocity in this dimension and the particles will get mutated. If the new solution isworse than the original solution after the operation of mutation and crossover, it will accepts theworse solution according to Metropolis criteria，otherwise it will rejects it. Hybrid particle swarmalgorithm combines the advantage of particle swarm optimization,differential evolution algorithmand simulated annealing algorithm, keep the particle diversity and has a strong practicality.A reactive power optimization mathematical model is build by taking minimum active powerloss in power system as objective function and transforms constrained optimization problems intounconstrained problem. Then it applies hybrid particle swarm algorithm to reactive poweroptimization, proposes some optimization solutions based on HPSO and simulates in IEEE14andIEEE30node system. By comparing the optimizing results with genetic algorithm and particleswarm algorithm, it proves applying HPSO algorithm to the reactive power optimization of powersystem is effective and has a good theoretical and practical value.