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)(參考版)
2025-05-16 11:59本頁面
【正文】 The symbols of the above equation and in the following context are given in the Nomenclature section. The minimization of the above function is subject to a number of constraints: QijijijijNj jiDiGi NiBGVVPPO i ????? ?? )s i nc os( ?? (2) PQijijijijNj jiDiGi NiBGVV i ????? ?? )c oss i n(0 ?? (3) and )()()()()(m a xm a xm inm a xm inm a xm inm a xm inlllCCiCiCiGGiGiGiTkkkBiiiNlSSNiQNiQNkTTTNiVVV?????????????? where power flow equations are used as equality constraints, reactive power source installation restrictions, reactive generation restrictions, transformer tapsetting restrictions, bus voltage restrictions and power flow of each branch are used as inequality constraints. In the most of the nonlinear optimization problems, the constraints are considered by generalizing the objective function using penalty terms. In the reactive power dispatch problem, the generator bus voltages and , the tap position of transformer, and the amount of the reactive power source installation are control variables which are selfconstrained. 教師評語 教師簽名： 。)。 。 英文文獻 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 20, NO. 2, MAY 2020 A MultiagentBased Particle Swarm Optimization Approach for Optimal Reactive Power Dispatch B. Zhao, C. X. Guo, and Y. J. Cao, Member, IEEE Abstract—Reactive power dispatch in power systems is a plex binatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, a solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented. This method integrates the multiagent system (MAS) and the particle swarm optimization (PSO) algorithm. An agent in MAPSO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a latticelike environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent 6 petes and cooperates with its neighbors, and it can also learn by using its knowledge. Making use of these agent–agent interactions and evolution mechanism of PSO, MAPSO realizes the purpose of optimizing the value of objective function. MAPSO applied to optimal reactive power dispatch 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 optimization strategy is general and can be used to solve other power system optimization problems as well. Index Terms—Multiagent system, particle swarm optimization (PSO), power system, reactive power dispatch NOMENCLATURE Voltage angle difference between buses and (rad). Transfer susceptance between bus and (.). Active power loss in work (.). Transfer conductance between bus and (.). Conductance of branch (.). Se
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