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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95998

    Title: 應用粒子群最佳化演算法於發電機組維修排程之研究
    Other Titles: Hydro-Thermal Generator Maintenance Scheduling via Particle Swarm Optimization Method
    Authors: 黃聰亮
    Contributors: 淡江大學電機工程學系
    Keywords: 維修排程;粒子群最佳化;均化備轉率;Maintenance scheduling;Particle swarm optimization;Levelize spinning reserve
    Date: 2004-10
    Issue Date: 2014-02-13 11:32:53 (UTC+8)
    Abstract: 發電機組的維修排程計畫,將隨著電業之自由化,愈趨複雜且重要。同時,這幾年來台灣地區經濟快速成長,使得用電量急遽增加,加上能源短缺及環保意識抬頭,使得電廠的興建受到阻力,致使備轉容量明顯低於20%。有鑑於此,如何能以現有的發電機組供給用戶可靠的電力,實屬重要。因此,有必要研擬一套可行之維修排程規劃方式,以利運用。在本計劃中,我們提出粒子群最佳化模型,來求解機組的維修排程問題。粒子群最佳化演算法是一種針對不同能量評估函數而能求得能量最小之最佳參數組的參數最佳驗算法。目前粒子群最佳化演算法已經成功的被應用在許多與排程及最佳化有關的工程實例上,與傳統的方法比較,粒子群最佳化演算法不僅可以減少處理時間,更能增加求解的精確性及可靠性。由測試系統結果顯示,粒子群最佳化演算法對於機組的維修排程問題而言,應不失為一個很好的求解工具。
    After electric utilities deregulation, maintenance scheduling is likely becoming more complicated and important. Recently, due to the rapid growth of load demand, and the difficulties of generating system expansion, spinning reserve of Taiwan power system now is obviously far below the acceptable level of 20%. How to provide a reliable electric power to the customers has become a more important issue. Therefore, it definitely needs a feasible planning method for maintenance scheduling. This project aims to investigate the capability of the Particle Swarm Optimization (PSO) in solving the maintenance scheduling problem. PSO are general optimal algorithm and have been successfully applied to many applications. Compared with other method, the PSO can not only reduce the processing time but also increase the accuracy and reliability during the solution process. Results obtained from a sample system show that the proposed PSO method might be a good solution method.
    Relation: 國科會電力學門九十二年度研究計畫成果發表會論文集,5頁
    Appears in Collections:[電機工程學系暨研究所] 會議論文

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