Introduction
Artificial Intelligence (AI) has revolutionized various industries, and the energy sector is no exception. Smart grids, which integrate AI technologies, have the potential to transform the traditional power grid into a more efficient, reliable, and sustainable system. One critical aspect of smart grids is their ability to automatically restore power after disruptions, such as natural disasters or equipment failures. This thesis focuses on the development of AI-based smart grid restoration strategies to enhance the resilience and reliability of power systems.
Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms
Chapter 2: Literature Review
2.1 Overview of Smart Grids
2.2 Role of AI in Smart Grids
2.3 Smart Grid Restoration Strategies
2.4 AI Techniques for Power System Restoration
2.5 Case Studies on AI-based Grid Restoration
2.6 Challenges and Opportunities in AI-based Grid Restoration
2.7 Integration of Renewable Energy Sources in Smart Grids
2.8 Resilience and Reliability in Power Systems
2.9 Existing Frameworks for Smart Grid Restoration
2.10 Gaps in Literature
Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 AI Algorithms Selection
3.5 Simulation Tools and Platforms
3.6 Case Study Design
3.7 Validation and Testing
3.8 Ethical Considerations
Chapter 4: Discussion of Findings
4.1 Analysis of AI-based Grid Restoration Strategies
4.2 Performance Evaluation Metrics
4.3 Comparison with Traditional Restoration Methods
4.4 Impact of AI on Resilience and Reliability
4.5 Scalability and Adaptability of AI-based Strategies
4.6 Cost-effectiveness and Efficiency
4.7 Implementation Challenges
4.8 Recommendations for Future Research
Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Limitations of the Study
5.5 Future Research Directions
5.6 Conclusion
Thesis Overview: The Development of AI-Based Smart Grid Restoration Strategies
The rapid advancement of AI technologies has opened up new possibilities for enhancing the resilience and reliability of power systems through smart grid solutions. This thesis explores the development of AI-based smart grid restoration strategies to automate the process of restoring power after disruptions. The literature review provides an overview of smart grids, the role of AI in grid restoration, existing frameworks, and challenges in the field. The research methodology outlines the design, data collection methods, AI algorithms selection, and case study design. The discussion of findings analyzes the performance, impact, scalability, and challenges of AI-based grid restoration strategies. The conclusion summarizes the findings, contributions, implications, limitations, and future research directions in the field. This thesis aims to contribute to the growing body of knowledge on AI-based solutions for enhancing the resilience and reliability of power systems in the context of smart grids.
