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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/6969

    Title: 整合粒子群最佳化方法與自組織式多項式網路於即時電力調度之研究(1/2)
    Combined particle swarm optimization and self-organizing polynomial networks for real-time power dispatch (1/2)
    Authors: 黃昭明
    Keywords: 雙目標電力調度
    bi-objective power dispatch
    real-time power dispatch
    particle swarm optimization
    evolutionary programming
    self-organizing polynomial networks
    Date: 2005-07-31
    Issue Date: 2009-12-31 16:37:38 (UTC+8)
    Abstract: 利用電力調度方式降低污染排放量為一種經濟有效的防治方法,此方法主要將污染量模型併入傳統經濟調度模式中,再藉由調度各發電機輸出電力的方式達到降低污染量的目的。為達成上述之最佳運轉目標,本計畫擬以兩年時間,以整體規劃、分年實施的方式進行研究。第一年應用粒子群最佳化方法求解考慮燃料成本與污染(NOx)排放之雙目標電力調度問題,其結果並與進化規劃法及傳統非線性規劃方法進行比較;另外,為使電力調度工作更適合於即時線上環境,第二年應用自組織式多項式網路於考慮燃料成本與污染排放之即時電力調度問題,其結果並將與傳統類神經網路方法進行比較。本計畫以IEEE 30 個匯流排、6 部發電機組、41 條傳輸線系統為例進行研究,以驗證所提出方法的可行性。結果顯示,本計畫第一年所提出之PSO 方法在解的品質及執行時間上均優於進化規劃法及傳統非線性規劃方法。
    It is an effective method to reduce the emissions by way of power dispatch. The method mainly integrates the emission function into traditional power dispatch model. Through the power dispatch of various generators, the purpose of reducing emission can be reached. To reach the purpose mentioned above, the project will be accomplished through an integrated schedule while executed in two separate years. In the first year, the project presents particle swarm optimization (PSO) method to solve the bi-objective power dispatch problems considering fuel cost and emissions(NOx). Comparisons have been made to the evolutionary programming (EP) and conventional non-linear programming (NLP) approaches. Besides, the second-year project adopts self-organizing polynomial networks (SOPN) to determine the real-time power dispatch problem. Results of the SOPN will be compared with artificial neural networks (ANN) method. The proposed approach has been verified on the IEEE 30-bus, 6-generator, and 41-transmission line system. Testing results indicate that the proposed PSO has high-quality solution and shorter computation time than the EP and NLP methods.
    Appears in Collections:[電機工程系所] 研究計畫

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