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博碩士論文 etd-0615115-141425 詳細資訊
Title page for etd-0615115-141425
論文名稱
Title
應用智慧型控制之彈性交流輸電裝置於大型再生能源併網電力系統之暫態穩定度研究
Applications of Intelligent Controllers for FACTS to Transient Stability Study of Large-Scale Renewable Energy Integrated with Power System
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
153
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
2015-07-09
繳交日期
Date of Submission
2015-07-15
關鍵字
Keywords
函數連結Elman神經網路、彈性交流輸電系統、基因蟻群演算法、自適應性智慧型控制系統、函數連結新式遞迴式模糊類神經網路、再生能源發電場
Flexible AC Transmission Systems, Genetic Ant Colony Optimization, Functional Link based Elman Neural Network, Renewable energy system, Functional Link based Novel Recurrent Fuzzy Neural Network, Adaptive Intelligent Control System
統計
Statistics
本論文已被瀏覽 5653 次,被下載 82
The thesis/dissertation has been browsed 5653 times, has been downloaded 82 times.
中文摘要
再生能源發電場具備大規模、非線性和不確定性等因素,一直是動態穩定控制中難以解決的問題。本文主要目的在探討複合式再生能源併網後對電力系統之動態與暫態影響。本文提出一種自適應性智慧型控制系統(Adaptive Intelligent Control System, AICS)應用於靜態同步補償器(Static Synchronous Compensator, STATCOM)與整合型電力朝流控制器(Unified Power Flow Controller, UPFC),用來增加具再生能源電場之系統動態穩定度與電力控制,並提出兩種自適應性智慧型控制系統類型:
1. 整合評斷類神經網路(Critic neural network, CNN)、函數連結新式遞迴式模糊類神經網路(Functional Link based Novel Recurrent Fuzzy Neural Network, FLNRFNN)、混合基因之時變性粒子群最佳演算法(Genetic Algorithm Hybrid Time Varying Particle Swarm Optimization, GAHTVPSO)所組成的控制系統。
2. 整合CNN、函數連結Elman神經網路(Functional Link based Elman Neural Network, FLENN)、基因蟻群演算法(Genetic Ant Colony Optimization algorithm, GACO)所組成的控制系統。本文同時將FLNRFNN、FLENN與CNN的鏈結權重值進行線上訓練。
在網路的學習速率選取方面,一般採用嘗試錯誤法來尋找適當之學習速率,由於此方法過於耗時,因此本文採用GAHTVPSO與GACO整合運用來調整網路學習速率,提高學習能力。最後,本文的模擬結果驗證上述所提出之自適應性智慧型控制系統可應用於再生能源併網之彈性交流輸電系統(Flexible Alternating Current Transmission Systems, FACTS)中,並可以實現較佳的阻尼特性以及態穩定性。
Abstract
The scale of nonlinearities and uncertainties of Renewable Energy System (RES) cause problems for dynamic control. This dissertation analyzes the dynamic and transient stability of the power system connected with a large RES including the analysis steady-state, dynamic and transient responses. An Adaptive Intelligent Control System (AICS) is proposed in the dissertation for a Static Synchronous Compensator (STATCOM) and a Unified Power Flow Controller (UPFC) to enhance the stability of the RES for power generation. An AICS can be used to increase the stability of the power control and improves the performance. Two models were proposed:
1. Integration of Critic neural network (CNN), Functional Link based Novel Recurrent Fuzzy Neural Network (FLNRFNN) and Genetic Algorithm Hybrid Time Varying Particle Swarm Optimization (GAHTVPSO) algorithm.
2. Integration of CNN, Functional Link based Elman Neural Network (FLENN) and genetic ant colony optimization algorithm (GACO).
The node connecting weights of the FLNRFNN, FLENN and CNN are trained online. The learning rates of the FLNRFNN, FLENN and CNN are usually selected by trial and error method and are time-consuming. The GAHTVPSO and GACO approach were developed to adjust the learning rates of FLNRFNN, FLENN and CNN to improve the learning rate. The validity of these algorithms was demonstrated with many simulations. The simulation results show that AICS can achieve better damping characteristics as well as transient stability in Flexible Alternating Current Transmission Systems (FACTS) applications.
目次 Table of Contents
論文審定書………………………………………i
致謝 …………………………………………ii
中文摘要…………………………………………iv
英文摘要…………………………………………v
目錄………………………………………………vi
圖目錄……………………………………………x
表目錄……………………………………………xiii

第一章 緒論……………………………………1

1-1 研究背景與動機……………………………1
1-2 論文貢獻…………………………………………6
1-3 論文內容概要…………………………………………7

第二章 風力與海岸波浪發電系統之模型…………………9

2-1前言…………………………………………9
2-2風力渦輪機模型…………………………………………9
2-3風力發電機模型…………………………………………13
2-3-1 PMSG模型…………………………………………13
2-3-2 DFIG模型…………………………………………16
2-4海岸波浪威爾斯渦輪機模型…………………………………………18
2-5海岸波浪感應發電機模型…………………………………………20

第三章 應用FACTS於風力發電場之設計……………………23

3-1前言…………………………………………24
3-2 STATCOM於離岸式風力發電場之設計………………………24
3-2-1 STATCOM操作原理…………………………………………24
3-2-2數學模型…………………………………………27
3-2-3 STATCOM補償控制…………………………………………33
3-2-4外部阻尼控制器…………………………………………35
3-3 UPFC於離岸式風力發電場之設計……………………………38
3-3-1 UPFC操作原理…………………………………………38
3-3-2數學模型…………………………………………41
3-3-2 UPFC之控制系統.…………………………………44
3-3-4 PID阻尼控制器…………………………………………48
3-4 本章結論…………………………………………49

第四章 自適應性智慧型控制系統之設計...………………………50

4-1前言…………………………………………50
4-2 STATCOM適應性智慧型控制系統之設計…………………………50
4-2-1自適應性智慧型控制系統…………………………………………50
4-2-2函數連結新式遞迴模糊類神經網路之架構……………52
4-2-3評斷網路之架構…………………………………………56
4-2-4學習與訓練程序…………………………………………58
4-2-5混合基因之時變性質粒子群最佳化演算法……………63
4-3 UPFC適應性智慧型控制系統之設計……………………69
4-3-1自適應性智慧型控制系統………………………………69
4-3-2函數連結Elman神經網路之架構………………………70
4-3-3評斷網路之架構…………………………………………73
4-3-4學習與訓練程序…………………………………………76
4-3-5基因混合蟻群最佳演算法………………………………79
4-4 本章結論…………………………………………83

第五章 模擬及結果分析…………………………………84

5-1 前言…………………………………………84
5-2 STATCOM於離岸風場之模擬…………………………………84
5-2-1 匯流排電壓變動之模擬分析………………………………86
5-2-2風速變動之模擬分析…………………………………………88
5-2-3故障之暫態模擬分析…………………………………………91
5-2-4 GAHTVPSO之測試…………………………………………95
5-2-5 多機系統之測試模擬分析……………………………………97
5-3 UPFC於離岸風場之模擬…………………………………………104
5-3-1 風速變動之模擬分析…………………………………………104
5-3-2故障之暫態模擬分析..………………………………………109
5-3-3 GACO之測試…………………………………………114
5-3-4 多機系統之測試模擬分析…………………………………116
5-4 本章結論…………………………………………123

第六章 結論及未來發展方向…………………………………………124

6-1 結論…………………………………………124
6-2 未來發展方向…………………………………………126

參考文獻…………………………………………127

附錄A…………………………………………135

附錄B…………………………………………138
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