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研究生:吳信賢
研究生(外文):Shinn-Shyan Wu
論文名稱:單區域電力系統控制效能標準與控制策略
論文名稱(外文):The Control Performance Standard and Control Strategies for a Single-Area Power System
指導教授:張簡樂仁
指導教授(外文):Le-Ren Chang-Chien
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:122
中文關鍵詞:單區域電力系統負載頻率控制控制效能標準自動發電控制
外文關鍵詞:AGCLFCSingle-area power systemCPS
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北美電力可靠度協會(North American Electric Reliability Council, NERC)在1997年公布了控制效能標準(Control Performance Standard, CPS)來取代舊有的控制效能準則(Control Performance Criteria, CPC),其用意為確保多區域電力系統的發電效能在一定水準之上。多區域系統評估控制效能標準的計算主要是依據系統頻率與區域間互聯線之電力潮流,然而,互聯線在單區域電力系統中並不存在。由此可知,北美電力可靠度協會所制定的控制效能標準並不適用於單區域系統。本文提出了新的單區域控制效能標準與其控制策略,並以長期的觀點來評估單區域系統效能。基於所提出的控制效能標準,本文進而提出兩種控制策略,以實現減少調頻機組的調校動作與強化系統的發電效能。透過Sugeno模糊推論系統(Sugeno Fuzzy Inference System)、遺傳基因演算法(Genetic Algorithm, GA)來實現並驗證本文所提出的控制策略。模擬結果證實了所提出的控制概念在負載頻率控制(Load Frequency Control, LFC)上的可行性。
In 1997, North American Electric Reliability Council (NERC) released the Control Performance Standard (CPS) to replace the Control Performance Criteria (CPC) to ensure the system performance for Interconnection. NERC‟s CPS which is adopted to evaluate the system performance is dependent on area‟s response of frequency, and tie-flow. However, tie-line does not exist in the single-area power system. Consequently, NERC‟s CPS may not suit for the regulation of an area without tie-flow support. This thesis presents a new control performance standard and its control strategies especially for the single-area power system. The new standard is used to assess system frequency performance in a long-term aspect. Based on the new standard, two operational concepts are proposed to achieve the objective of both unit maneuvering relaxation and control performance. With the aid of Sugeno fuzzy inference system, and multi-objective optimization using genetic algorithm, the proposed concepts are successfully confirmed and implemented in the simulation cases. Simulation results validate the effectiveness of the proposed strategies in the performance of the load frequency control.
Chinese Abstract ... i
Abstract ... ii
Acknowledgements ... iii
List of Figures ... viii
List of Tables ... xi
Nomenclature ... xii
Chapter 1 Control Performance Standard Overview ... 1
1.1 Introduction ... 1
1.2 Control Performance Criterion ... 3
1.2.1 A1 Criterion ... 3
1.2.2 A2 Criterion ... 4
1.3 Control Performance Standard ... 7
1.3.1 Control Performance Standard 1, CPS1 ... 7
1.3.2 Control Performance Standard 2, CPS2 ... 11
1.3.3 Advantage of CPS ... 15
1.4 State-of-the-art CPS ... 16
Chapter 2 Single-Area Power System Load Frequency Control ... 19
2.1 Generator Model ... 21
2.2 Load Model ... 26
2.3 Prime-Mover Model ... 27
2.4 Governor Model ... 29
2.5 Automatic Generation Control ... 35
Chapter 3 Single-Area Control Performance Standard ... 38
3.1 Target Frequency Bound ... 38
3.1.1 Statistical Tools-PDF, Standard Deviation, Mean, and RMS ... 39
3.2 Target Frequency Bound for Taiwan Power System ... 41
3.3 Derivation of Single-Area‟s Performance Standard ... 44
3.3.1 Formulation ... 45
3.3.2 Compliance ... 46
3.3.3 Advantage of CPSSA ... 47
3.4 Moving Average ... 47
3.5 Control Strategies on Single-Area Control Performance Standard ... 51
3.5.1 Unit Maneuvering Relaxation-Oriented Control ... 52
3.5.2 Performance-Oriented Control ... 60
3.5.3 Tuning Trend Formulation .... 60
Chapter 4 Apply Artificial Intelligence Techniques to Control Strategies ... 70
4.1 Using Fuzzy System for Dynamic Control Threshold Adjustment ... 70
4.2 Membership function ... 75
4.3 If-Then Rules ...77
4.4 Fuzzy Inference System ... 80
4.4.1 Step 1. Fuzzy Inputs ... 81
4.4.2 Step 2. Apply Implication Method ... 82
4.4.3 Step 3. Aggregate All Outputs ... 83
4.4.4 Step 4. Defuzzification ... 84
4.5 Sugeno-Type Fuzzy Inference System ... 86
4.5.1 Comparison of Sugeno and Mamdani Methods ... 88
4.5.2 Apply Sugeno Fuzzy Model to CF Control Threshold ... 89
4.5.2.1 Define the Fuzzy Rules ... 89
4.5.2.2 Construct the Membership Functions ... 91
4.5.2.3 Calculate the Dynami CF Control Threshold ... 96
4.5.3 Simulation ... 96
4.6 Using Genetic Algorithm for Optimal Gain Tuning ... 100
4.6.1 Methodology ... 100
4.6.2 Encoding ... 102
4.6.3 Chromosome ... 102
4.6.4 Decoding ... 102
4.6.5 Fitness Function and Parameters ... 103
4.6.6 Initial Population ... 103
4.6.7 Reproduction ... 104
4.6.8 Crossover ... 105
4.6.9 Mutation ... 106
4.6.10 Termination Criterion ... 106
4.6.11 Multi-Objective Optimization ... 107
4.7 Simulation .... 108
Chapter 5 Conclusion and Future Works ... 112
Bibliography ... 114
Appendix ... 118
Vita.... 122
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