TABLE OF CONTENTS
TABLE OF CONTENT
LIST OF ABBREVIATION
ABSTRACT
CHAPTER ONE: INTRODUCTION
1.1 Background
1.2 Statement of the Problem
1.3 Aim and Objectives
1.4 Methodology
1.5 Significance Contribution
1.6 Thesis Organization
CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction
2.2 Conventional Approach
2.2.1 Determination of Radial Configuration
2.1.2 Kirchhoff Matrix Tree Theorem
2.1.3 Heuristic Approach to Distribution Network Reconfiguration
2.1.3.1 Branch-and-Bound Technique
2.1.3.2 Optimal Flow Pattern
2.1.3.3 Ruled-Based Comprehensive Approach
2.1.4 Graph Theory Concept and Application to Reconfiguration
2.1.5 Mixed Integer Linear Programming Approach
2.1.5.1 Simplified Mathematical Model of Distribution Network Reconfiguration
2.1.5.2 Mixed-Integer Linear Model
2.1.6 Application of Genetic Algorithm to Network Reconfiguration
2.1.7 Spanning Tree
2.1.8 Matroid Theory to Reconfiguration
2.1.9 Depth First Search Algorithm
2.1.10 Overview of Meta-Heuristic Technique for Network Reconfiguration
2.1.10.1 Application Ant Colony optimization for Network Reconfiguration
2.1.10.2 Application of Particle Swarm Optimization for Network Reconfiguration
2.1.11 Application of Multi Objective Evolutional Programming for Network Reconfiguration
2.1.11.1 Sum of Weighted Cost Function
2.1.11.2 Multi objective Evolution Programming Using Fuzzy Objective Function
2.1.11.3 Pareto Optimal Concept
2.1.11.4 Fast and Elitist Non-Dominated Sorting Genetic Algorithm (NSGA II)
2.2 REVIEW OF SIMILAR WORKS
CHAPTER THREE: MATERIALS AND METHODS
3.1 Introduction
3.2 Model Equation of the Two Bus Distribution Network
3.3 Load Model
3.3.1 Constant Power Load (CP)
3.3.2 Constant Current Load (CI)
3.3.3 Constant Impedance Load (CZ)
3.3.4 Exponential Load
3.4 Modelling of Distributed Generation
3.4.1 Constant Active and Reactive Power Model
3.4.2 Constant Active Power and Constant Voltage Model
3.5 Distributed Generation placement
3.6 Developed Approach
3.6.1 Data Collection
3.6.2 Determination of Initial Configuration
3.6.2 Formulation of the Problem
3.7 Conclusion
CHAPTER FOUR: RESULTS AND DISCUSSIONS
4.1 Introduction
4.2 Comparative Study Before and After the Application of Reconfiguration
4.2.1 Gaskiya 16-Bus Network
4.2.2 Railway 19-Bus Network
4.2.3 Sabo 29-Bus Distribution Network
4.2.4 Canteen 50-Bus Network
4.2.5 Discussion
4.3 Analysis of the Voltage Profile
4.3.1 Gaskiya 16-Bus Network
4.3.2 Railway 19-Bus Network
4.3.3 Sabo 29-Bus Network
4.3.4 Canteen 50-Bus Network
4.4 Conclusion
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
5.2 Conclusion
5.3Limitations
5.4 Recommendation for Further Work
Reference
Appendix
ABSTRACT
This worked aimed at developing an optimal reconfiguration algorithm for a radial distribution network using fast and elitist non-dominated sorting genetic algorithm (NSGA II), considering distributed generation. The work models the reconfiguration using a pragmatic multi-objective approach considering active power loss and total voltage deviation, so as to determine the optimum locations of the tie and sectionalizing switches within the distribution network. The model was validated using standard IEEE 33-Bus network and extended to a subsection of Zaria distribution network. The active power loss and total voltage deviation was estimated for Gaskiya, Railway, Sabo and Canteen distribution network as 55.32kW, 0.22V, 17.22kW, 0.2340V, 120.08kW, 0.9949V and 508.0kW, 4.7482V respectively, prior to reconfiguration. With distributed generation placed at different location based on the computed voltage stability index of the nodes. The active power loss for Gaskiya, Railway, Sabo and Canteen distribution network was recorded to be 26.19%, 19.22%, 10.23% and 8.01% reduction respectively as compared to the initial configuration with distributed generation placed at strategic locations. The optimal location of the tie and sectionalizing switches for Gaskiya, Railway, Sabo and Canteen distribution network was found to be 12 14, 14 16 18, 25 20 11 and 23 16 43, respectively after reconfiguration. A reduction in active power loss and reductions in total voltage deviation for Gaskiya, Railway, Sabo and Canteen distribution network was found to be 37.64%, 28.94%, 18.2%, 8.12%, 39.21%,37.8% and 23.42%, 10.72% respectively as compared to the active power loss and total voltage deviation of the initial configuration.
CHAPTER ONE
INTRODUCTION
1.1 Background
The concept of distribution system automation has capture the attention of researchers and utility companies over the last decade. Even though, a lot of research have been undertaken and many works are still ongoing at the moment on “distribution system automation”, due to technological innovations and increasing connection of distributed energy source in to existing distribution network. The research is aimed at developing an optimal reconfiguration model for a radial distribution network using Zaria distribution network as a case study.
The problem of minimizing distribution systems losses has received a global attention due to high cost of electrical energy, need for better quality of service, efficient utilization of available energy and high power loss in power networks (Charlangsut et al, 2012). Under normal operating conditions, any distribution network must supply electrical power to all its customer connected to it, while simultaneously avoiding overloading, feeder thermal overload and abnormal voltage across the line, as well as minimizing active power loss and maintaining radial topology (Carcamo-Gallardo et al, 2008) etc. There are different techniques for reducing losses within the distribution level which include reconfiguration, capacitor placement, load balancing, introduction of higher voltage level and reconductoring (Abdelaziz et al, 2010; Sarfi et al, 1994). These methods of reducing losses are quite numerous but the major concern is about their technical implications on the network. Introduction of new equipment at distribution level offers tremendous financial burden on the utilities that cannot be justified by the potential saving. The use of fixed.....
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