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Introduction

The development of a Smart Grid Management System using Artificial Intelligence (AI) has become a crucial aspect in ensuring efficient and reliable power distribution in modern-day power systems. With the increasing demand for electricity and the integration of renewable energy sources into the grid, traditional grid management systems are proving to be inadequate in meeting the challenges posed by these changes. In response to this, the use of AI technologies such as Machine Learning and Deep Learning has emerged as a promising solution to optimize grid operations, improve system stability, and enhance energy efficiency.

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 Two: Literature Review
2.1 Evolution of Smart Grid Technology
2.2 Role of Artificial Intelligence in Smart Grid Management
2.3 Applications of AI in Power Systems
2.4 Challenges in Smart Grid Management
2.5 Existing AI-based Grid Management Systems
2.6 Comparative Analysis of AI Techniques in Grid Management
2.7 Integration of Renewable Energy Sources
2.8 Cybersecurity in Smart Grid Systems
2.9 Future Trends in Smart Grid Management
2.10 Summary of Literature Review

Chapter Three: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Implementation of AI Algorithms
3.4 Optimization Techniques
3.5 Real-time Monitoring and Control
3.6 Integration with Existing Grid Infrastructure
3.7 System Testing and Validation
3.8 Performance Evaluation Metrics

Chapter Four: System Implementation
4.1 Hardware and Software Requirements
4.2 Data Acquisition and Storage
4.3 AI Model Development
4.4 Integration with SCADA Systems
4.5 Cloud Computing and Big Data Analytics
4.6 User Interface Design
4.7 Deployment in Pilot Grid
4.8 System Maintenance and Upgrades

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Achievements and Limitations
5.3 Recommendations for Future Research
5.4 Conclusion

Thesis Overview:

In recent years, the power sector has witnessed a significant transformation with the rapid integration of renewable energy sources and the advent of advanced technologies such as Artificial Intelligence (AI). This thesis focuses on the development of a Smart Grid Management System using AI to address the challenges faced by modern power systems. The study aims to design an intelligent system that can optimize grid operations, improve energy efficiency, and enhance system stability.

The thesis begins with an introduction that provides a background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review in Chapter Two explores the evolution of smart grid technology, the role of AI in grid management, applications of AI in power systems, challenges, existing systems, comparative analysis, integration of renewables, cybersecurity, and future trends.

Chapter Three discusses the system design and methodology, including system architecture, data collection, AI implementation, optimization, monitoring, integration, testing, and evaluation. Chapter Four focuses on the implementation of the system, covering hardware/software requirements, data acquisition, AI model development, SCADA integration, cloud computing, user interface, deployment, and maintenance.

In Chapter Five, the thesis concludes with a summary of findings, achievements, limitations, recommendations for future research, and a comprehensive conclusion. Overall, the development of a Smart Grid Management System using AI has the potential to revolutionize the power sector and pave the way for a more sustainable and efficient grid system.

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