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)-資料下載頁(yè)
2025-05-11 11:59本頁(yè)面
【導(dǎo)讀】括多重性局部微小反應(yīng)和非線性斷續(xù)的系統(tǒng)規(guī)定參數(shù)。在這篇論文中，提出了一個(gè)針對(duì)。穎的微粒子群最優(yōu)化途徑。這個(gè)方法融合了多主體模式和新穎的粒子群最優(yōu)化。MAPSO中的一個(gè)主體代表著對(duì)于PSO的一種微粒和對(duì)于最優(yōu)化問題的一種候選。所有的主體都存在于一個(gè)格子狀的環(huán)境中，也就是說格子內(nèi)每一個(gè)交叉點(diǎn)上。為了快速地匯集所有的積極因素，每一個(gè)主體會(huì)完成和鄰居的合作，它。們也可以學(xué)習(xí)運(yùn)用自己的知識(shí)。為了更好地利用主體和主體之間的反互相作用和PSO的。發(fā)展構(gòu)造，MAPSO充分認(rèn)識(shí)到了這種積極反應(yīng)的價(jià)值。MASPO表示，積極的能源反應(yīng)發(fā)射。裝置可以分為為IEEEE30倍功率模式和實(shí)用的118倍功率模式。道的方案相比，文中所提出的方法更快更有效。這種最優(yōu)化方案適用性較廣，可以用于。NB設(shè)定的所有總線的數(shù)量。NlimQ設(shè)定超出無功能量極限之外的總線的數(shù)量。盡管PSO看起來比一些重量和參數(shù)的調(diào)整更容易受影響，許多
【正文】 ed 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 power dispatch. Section III describesMAPSO in detail. Simulation results and parison with other approaches are given in Section IV. Finally, conclusions are presented in Section V. II. PROBLEM FORMULATION 10 The objective of the reactive power dispatch is to minimize the active power loss in the transmission work, which can be described as follows: )c os2( 22 ijjijNk iNk k l o s sQ VVVVgkPfEE????? ?? ?? (1) where iB NjNijik ??? 。)。,( 。 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(píng)語 教師簽名：
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