本文選題：云模型 + 遷移操作��； 參考：《電網(wǎng)技術(shù)》2014年12期

【摘要】：針對粒子群算法在求解無功優(yōu)化問題時存在早熟收斂,易陷于局部最優(yōu)的現(xiàn)象,提出了自學(xué)習(xí)遷移粒子群算法(self-learning migration particle swarm optimization,SLMPSO)。該算法在采用混沌序列對粒子群進(jìn)行初始化操作,基于云模型理論的X-條件云發(fā)生器對粒子的慣性權(quán)重進(jìn)行自適應(yīng)調(diào)整的基礎(chǔ)上,引入一種遷移操作,以引導(dǎo)全局最優(yōu)粒子的飛行方向,解決粒子群后期朝單一進(jìn)化方向進(jìn)化的問題,有效地增強(qiáng)了算法的全局尋優(yōu)能力。針對電力系統(tǒng)無功優(yōu)化中的離散變量歸整問題,首先將離散變量完全化為連續(xù)變量進(jìn)行迭代求解,在尋求至全局最優(yōu)解后引入高斯罰函數(shù)對離散變量進(jìn)行歸整操作。以網(wǎng)損和電壓偏離最小為目標(biāo),對IEEE標(biāo)準(zhǔn)30節(jié)點(diǎn)算例進(jìn)行仿真計(jì)算,驗(yàn)證了所提算法的有效性和可行性。
[Abstract]:Aiming at the phenomenon that particle swarm optimization (PSO) has premature convergence and is prone to be trapped in local optimum in solving reactive power optimization problem, a self-learning migration particle swarm optimization (SLMPSOO) algorithm is proposed in this paper. This algorithm introduces a migration operation on the basis of initializing particle swarm by chaotic sequence and adjusting the inertia weight of particle by X- conditional cloud generator based on cloud model theory. In order to guide the flight direction of the global optimal particle and solve the problem that the particle swarm evolves towards a single evolutionary direction in the later stage, the global optimization ability of the algorithm is effectively enhanced. In order to solve the problem of discrete variables in reactive power optimization, the discrete variables are completely transformed into continuous variables to be solved iteratively, and Gao Si penalty function is introduced to correct the discrete variables after seeking the global optimal solution. Aiming at minimum network loss and voltage deviation, the IEEE standard 30-node example is simulated to verify the effectiveness and feasibility of the proposed algorithm.
【作者單位】： 武漢大學(xué)電氣工程學(xué)院;貴州電力試驗(yàn)研究院;
【基金】：國家科技支撐計(jì)劃(2013BAA02B02)~~
【分類號】：TM714.3

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本文編號：1803781