Reactive Power Optimization in Power System using Genetic Algorithm: A Case Study of Choba Distribution Network, Nigeria.
- 주제(키워드) Electricity losses
- 주제(DDC) 333.79
- 발행기관 아주대학교 국제대학원
- 지도교수 Chai Won Kwon
- 발행년도 2024
- 학위수여년월 2024. 2
- 학위명 석사
- 학과 국제대학원 융합에너지학과
- 실제URI http://www.dcollection.net/handler/ajou/000000033229
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
An increase in inductive loads is driving up energy consumption to the point where distribution system analysis is becoming too complicated to answer theoretically. Nigeria's electrical distribution system so regularly finds it difficult to keep up with the nation's high energy consumption. To maximise power losses and increase operating bus voltages, this research study analyses the Choba 11kV Distribution Network and applies a genetic algorithm to reactive power optimisation. A load flow analysis of the 76 buses in the network under inquiry showed that 41 of them were running at a critical level of undervoltage. Additionally, a genetic algorithm was used to position capacitors optimally to enhance system performance overall and guarantee the dependability of power supply. After inserting the capacitor banks, a load flow analysis was carried out to validate the proposed methodology for reactive power optimisation. The results showed that all buses were operating within permissible limits, that the active power losses had decreased by 30.9%, and that the reactive power losses had decreased by 31.5%, improving the distribution system's performance. The power system network was modelled and simulated using ETAP (Electrical Transient Analyzer Program) software, while load flow analysis was conducted using Newton Raphson's approach.
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CHAPTER 1: INTRODUCTION 1
1.1. Background of the Study 1
1.2. Statement for the Research Problem 4
1.3. Purpose of the Study 4
1.4. Aim of the Study 5
1.5. Objectives of the Study 5
1.6. Significance of the Study 5
1.7. Outline of the Dissertation 5
1.8. Limitation of the Study 6
CHAPTER 2: LITERATURE REVIEW 7
2.1. Related work on Reactive Power Optimization . 8
2.2. Administrative Losses 10
2.2.1. Reasons for Administrative Losses . 10
2.2.1.1. From the perspective of Organization 10
2.2.1.2. From the perspective of Consumers 11
2.2.1.3. From the perspective of External Elements 11
2.2.2. Remedial Measures to control Administrative Losses . 11
2.3. Distribution Losses 11
2.3.1. Measures to reduce Technical Losses 13
2.4. Aggregate Technical, Commercial and Collection Losses (ATC&C) . 14
2.4.1. Measures to reduce Aggregate Technical, Commercial and Collection Losses 14
2.5. Identified Research Gap 14
CHAPTER 3: MATERIALS AND METHOD 16
3.1. Fundamental equations for Load Flow Analysis on N-bus System 16
3.2. Description of Existing Network 18
3.3. Newton Raphson's Load Flow Analysis Method 21
3.3.1. Procedures for performing Newton-Raphson's Load Flow Analysis method 22
3.3.2. Detailed flow chart for Newton Raphson Load Flow Method 23
3.4. Analysis of the Network Improvement Technique 24
CHAPTER 4: RESULTS AND DISCUSSION 26
4.1. Existing Load Flow Analysis of Choba 11kV Distribution Network 26
4.2. Optimal Capacitor Placement using Genetic Algorithm . 31
4.3. Load Flow Analysis after Optimal Capacitor Placement 33
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS . 40
5.1. Conclusion . 40
5.2. Recommendations 41
REFERENCES . 42
APPENDIX I . 45
APPENDIX II 50
