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博碩士論文 etd-0717103-175415 詳細資訊
Title page for etd-0717103-175415
論文名稱
Title
應用類神經網路於電力系統卸載策略之研究
A Study on Load Shedding of Power Systems by Using Neural Networks
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
93
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2002-06-14
繳交日期
Date of Submission
2003-07-17
關鍵字
Keywords
類神經網路、倒傳遞、卸載策略、暫態穩定度、電力系統
back propagation, power system, neural network, load shedding, transient stability
統計
Statistics
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The thesis/dissertation has been browsed 5676 times, has been downloaded 3457 times.
中文摘要
本論文主要在探討以類神經網路之函數估測功能應用於電力系統之最小卸載量預測,文中選擇國內一實際之汽電共生廠與台電系統為研究對象,詳細介紹此二系統之網路架構並建立其發電機組、激磁系統、調速系統及負載的控制模型。接著推導倒傳遞類神經網路之數學模型,建立其網路架構與訓練方法以便將類神經網路應用於電力系統之卸載策略。在考量系統各種不同運轉與故障狀況下,執行暫態穩定度分析以獲得系統實際之最小卸載量,接著選擇發電機發電量、負載量、頻率下降率及卸載量作為類神經網路的訓練資料,使用倒傳遞網路配合L-M學習法,估算出系統在各種不同狀態下的最小卸載量。最後對汽電共生與台電系統各選擇三種運轉故障事例進行模擬,執行含傳統卸載與類神經網路卸載的暫態穩定度分析。由模擬結果可知,本文所建議之類神經網路卸載確實可在維持系統穩定運轉的前提下,切除最少的負載量,達成適應性卸載之目的。
Abstract
This objective of thesis is to derive the adaptive load shedding by artificial neural network (ANN) so that the amount of load shedding can be minimized. An actual industrial customer and Taipower system are selected for computer simulation to fit the ANN model. The mathematical models of generation, exciters, governors and loads are used in the simulator program. The back propagation neural method is considered for the neural network training of load shedding.To create the training data set for ANN models, the transient stability analysis is performed to fit the load shedding under different operation and fault condition. The back propagation method and L-M learning process are then used to fit the minimum load shedding without causing system stability problem. To verify the effectiveness of the proposed methodology for adaptive load shedding, three fault contingencies for both the industrial cogeneration system and Taipower system have been simulated. By compare to the conventional load shedding, it is found that the amount of load shedding can be minimized and adjusted according to the real time operation conditions of power systems.
目次 Table of Contents
第一章緒論
1.1 研究背景與動機
1.2 研究概要與章節簡述
第二章電力系統暫態穩定度
2.1 暫態穩定度定義
2.2 暫態穩定度分析
2.2.1 系統模型之建立
2.2.2 故障前系統穩態初值之計算
2.2.3 故障後系統動態之計算
2.3 電力系統傳統卸載模式
第三章類神經網路
3.1 神經元簡介
3.2 類神經網路架構
3.3 類神經網路之訓練模式
3.3.1 倒傳遞式演算法(Back-Propagation Algorithm)
3.3.2 Levenberg-Marquardt Back-Propagation 演算法(L-M BP)
3.4 類神經網路應用於電力系統卸載策略
第四章電力系統卸載分析
4.1 汽電共生廠網路架構紹
4.2 汽電共生廠電力潮流分析
4.3 以暫態穩定度分析執行汽電共生系統之卸載策略模擬
4.3.1 汽電廠之卸載策略
4.3.2 以暫態穩定度分析模擬汽電共生系統不同卸載策略
4.4 台電系統網路架構
4.5 台電系統電力潮流分析
4.6 以暫態穩定度分析執行台電系統之卸載策略模擬
4.6.1 台電系統之卸載策略
4.6.2 以類神經為基礎之台電系統卸載策略
4.6.3 以暫態穩定度分析模擬台電系統不同卸載策略
第五章結論
5.1 結論
5.2 未來之研究方向
參考文獻
參考文獻 References
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