外文翻譯(基于多主體粒子群最優(yōu)化能源反應(yīng)發(fā)生裝置的研究)(存儲版)
【正文】 works. In the last decade, many new stochastic search methods have been developed for the global optimization problems, such as geic algorithms, evolutionary programming and particle swarm optimization. Particle swarm optimization (PSO) is one of the evolutionary putation techniques [11]. It was developed through simulation of a simplified social system, and has been found to be robust in solving continuous nonlinear optimization problems. The PSO technique can generate highquality solutions within shorter calculation time and have more stable convergence characteristic than other stochastic methods. Although the PSO seems 9 to be sensitive to the tuning of some weights or parameters, many researches are still in progress to prove its potential in solving plex power system problems [12], [13]. Kassabalidis et al. [14] introduced dynamic security border identification using enhanced PSO. Naka et al. [15] proposed a hybrid PSO for distribution state estimation. It has been found that the PSO quickly finds the highquality optimal solution for many power system optimization problems. Generally, PSO has a more global searching ability at the beginning of the run and a local search near the end of the run. Therefore, while solving problems with more local optima, there are more possibilities for the PSO to explorelocal optima at the end of run. However, the reactive power optimization problem does have these properties in itself. For these reasons, a reliable global approach to power system optimization problems would be of considerable value to power engineering munity. Recently, agentbased putation has been studied in the field of distributed artificial intelligence [16] and has been widely used in other branches of puter science [17]. Problem solving is an area that many multiagentbased applications are concerned with. Liu et al. [18] introduced an application of distributed techniques for solving constraint satisfaction problem. Enlightened by multiagent system and PSO, this paper integrates multiagent system and PSO to form a multiagentbased PSO approach (MAPSO), for solving the reactive power optimization problem. In MAPSO, an agent represents a particle to PSO and a candidate solution to theoptimization problem. All agents live in a latticelike environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, they pete and cooperate with their neighbors, and they can also use knowledge. Making use of these agent–agent interactions and evolution mechanism of PSO in a latticelike environment, the proposed method can find highquality solutions reliably with the faster convergence characteristics in a reasonably good putation time. MAPSO applied for optimal reactive power is evaluated on an IEEE 30bus power system and a practical 118bus power system. Simulation results show that the proposed approach converges to better solutions much faster than the earlier reported approaches. The rest of this paper is organized as follows: Section II describes mathematical formulation of optimal reactive po
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