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題 名 | 配電饋線電容器規劃=Distribution Feeder for Capacitors Planning |
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作 者 | 康渼松; 曾燕明; 曾本立; | 書刊名 | 高苑學報 |
卷 期 | 7:1 1998.02[民87.02] |
頁 次 | 頁81-85 |
分類號 | 448.3 |
關鍵詞 | 饋線; 電容器; Distribution feeder; Capacitors planning; |
語 文 | 中文(Chinese) |
中文摘要 | 本論文所提出類神經網路進行電容器規劃,利 用最小單位的實際使用電容器,以各小時為單位, 以饋線負載預測值為基礎,逐次加裝電容器至各虛 功補償區段(點)上,直到饋線實功損失不再降低為 止。於損失比較的過程中,饋線損失是由類神經網 路所估算出來。類神經網路在經由訓練之後,能直 接以各區段的虛實功值估算出整個饋線的損失量。 其速度與過程均較以傳統負載潮流程式方法執行為 快速且簡易。本文提出電容器規劃方式,在虛功及 實功預測與電容器規劃上均採用簡易的類神經網路 方法,避免迴歸分析、潮流分析與最佳化規劃等繁 雜計算過程,且可達到一定的成效。經實際饋線之 測試,證實本文所得方法確實能有效降低饋線的實 功損失。 |
英文摘要 | With the hourly loading information derived, the reactive power compensation is solved by considering the minimum unit capacity of distribution capacitors used in distribution system. The real and reactive power loss of line feeder section was also solved easily by neural networks. By the methodology proposed, the reactive power compensation can be obtained in a very efficiently manner Because the neural network is used in this study, the complicated process of conventional reactive power compensation by regression analysis, load flow analysis and optimization procedures can therefore be prevented. According to the computer simulation, it is concluded that the neural network can be applied to the proper capacitor installation to reduce feeder loss effectively. |
本系統中英文摘要資訊取自各篇刊載內容。