本文關(guān)鍵詞： 梯級水電站群 優(yōu)化調(diào)度 多目標優(yōu)化 量子粒子群算法 混沌變異 外部檔案集合　出處：《水力發(fā)電學報》2017年05期 　論文類型：期刊論文

【摘要】：為科學求解梯級水電站群多目標優(yōu)化調(diào)度模型,提出一種基于量子行為進化機制的多目標量子粒子群算法(MOQPSO)。該方法以標準量子粒子群算法(QPSO)為基礎(chǔ),引入外部檔案集合存儲非劣粒子,利用個體支配關(guān)系實現(xiàn)檔案集合的動態(tài)更新維護;依據(jù)個體領(lǐng)導(dǎo)能力優(yōu)劣選擇粒子歷史最優(yōu)位置與種群全局最優(yōu)位置,維持搜索過程中個體進化方向的多樣性;采用混沌變異算子對個體進行局部擾動,提升算法的全局收斂性能。烏江流域模擬調(diào)度結(jié)果表明,所提方法具有良好的收斂速度與尋優(yōu)能力,可快速獲得兼顧梯級水電系統(tǒng)經(jīng)濟性與可靠性要求的Pareto解集,能夠為工程人員提供科學的決策依據(jù)。
[Abstract]:In order to solve the multi-objective optimal dispatching model of cascade hydropower station group scientifically. A multi-objective quantum particle swarm optimization algorithm based on quantum behavior evolution mechanism is proposed, which is based on standard quantum particle swarm optimization (QPSO). The external file set is introduced to store the non-inferior particles, and the dynamic updating and maintenance of the file collection is realized by using the individual domination relation. According to the individual leadership ability, the historical optimal position of particle and the global optimal location of population are selected to maintain the diversity of individual evolutionary direction in the process of searching. Chaotic mutation operator is used to make local disturbance to improve the global convergence performance of the algorithm. The simulation results show that the proposed method has a good convergence speed and optimization ability. The Pareto solution set which takes into account the requirements of economy and reliability of cascade hydropower system can be obtained quickly, and it can provide scientific decision basis for engineers.
【作者單位】： 大連理工大學水電與水信息研究所;
【基金】：國家自然科學基金(91547201;51210014) 國家重點基礎(chǔ)研究發(fā)展計劃(973計劃)資助項目(2013CB035906)
【分類號】：TV737
【正文快照】： 單元[1-2]。發(fā)電量(發(fā)電效益)最大等單目標調(diào)度0引言模型僅能從某一方面考慮梯級總體發(fā)電效益最大伴隨我國各大流域巨型水電站的相繼投產(chǎn)運化,未能有效計及豐枯峰谷時段的特性差異,極易行以及全國互聯(lián)智能電網(wǎng)的平穩(wěn)有序推進,梯級水造成電能在年內(nèi)的非均衡分布,甚至引發(fā)部分時

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