Reactive Power Optimization And Application In Fuliang Electric Power Company

During the "Twelfth Five-Year", the government proposed energy saving andenvironmentally friendly low-carbon power grid construction strategy, Reactive poweroptimization is a strong means for improving economic and stability. It is one of themost important aspects which needs to pay attention to in the process of thedevelopment of smart grid.Particle swarm optimization has certain advantages to solve high-dimensional,large-scale, non-linear function optimization problems. The control variables includecontinuous variables and mixed variables and there are many nodes in the powersystem. Particle swarm optimization is selected as the basic optimization algorithm。Particle swarm optimization in the optimization process has a fatal flaw: easy tofall into local optimum. If there is several local optimal solutions, the particle swarmoptimization is very easy to fall into a local optimal solution and misses the globaloptimal solution throughout the optimization process. Based on particle swarmoptimization, this article prompts two improved algorithms. An improved ParticleSwarm Optimization (ISIPSO) algorithm was proposed and first applied in reactivepower optimization in this paper. ISIPSO adjusts learning factors to obtain rational andeffective search speeds. Applying the information sharing strategy increases thediversity of the population. Introducing disturbance term to the process of locationupdating avoid the algorithm fall into local optima, also accelerate the convergenceThe new algorithms are implemented on the IEEE-14bus system and comparedwith other optimization algorithms’, the results show that ISIPSO has stronger globaloptimal searching ability, faster convergence rate, and better robustness and can moreeffectively solve the reactive power optimization problem in power system. An area ofsmall power system in Fuliang County, Jiangxi province, is optimized by thealgorithm; the system data comparison before and after the optimization, to verify theeffectiveness and the feasibility of the proposed optimization algorithm.