The Advanced Electric Power Grid Complexity Reduction Techniques for Reliability Modeling 1st edition by Ayman Faza, Sahra Sedigh, Bruce McMillin ISBN 3540876977 9783540876977
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Authors:Ayman Z. Faza, Sahra Sedigh; Bruce M. McMillin , Tags:Computer Safety; Reliability; and Security , Author sort:Ayman Z. Faza, Sahra Sedigh & McMillin, Bruce M. , Languages:Languages:eng , Comments:Comments:Computer Safety, Reliability, and Security
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ISBN 10: 3540876977
ISBN 13: 978-3540876977
Author: Ayman Z. Faza, Sahra Sedigh, Bruce M. McMillin
The power grid is a large system, and analyzing its reliability is computationally intensive, rendering conventional methods ineffective. This paper proposes techniques for reducing the complexity of representations of the grid, resulting in a mathematically tractable problem to which our previously developed reliability analysis techniques can be applied. The IEEE118 bus system is analyzed as an example, incorporating cascading failure scenarios reported in the literature.
The Advanced Electric Power Grid: Complexity Reduction Techniques for Reliability Modeling 1st Table of contents:
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Introduction
- Overview of the Advanced Electric Power Grid
- Importance of Reliability in Power Grid Systems
- Complexity Challenges in Power Grid Modeling
Fundamentals of Power Grid Reliability
- Reliability Concepts and Metrics
- Components and Architecture of the Power Grid
- Types of Failures and Reliability Issues in Power Grids
Complexity in Power Grid Modeling
- Sources of Complexity in Power Grid Models
- Traditional Methods of Reliability Modeling
- Challenges in Handling Large-Scale Grid Systems
Complexity Reduction Techniques
- Decomposition Methods for Large-Scale Systems
- Approximation and Simplification Strategies
- Stochastic Modeling for Complexity Reduction
Techniques for Reliability Evaluation
- Probabilistic Methods in Reliability Assessment
- Monte Carlo Simulations and Their Application
- Analytical Methods for Reliability Calculation
Advanced Modeling Approaches
- Hybrid Models for Grid Reliability
- Adaptive and Dynamic Modeling Techniques
- Integration of Grid Control and Reliability
Applications and Case Studies
- Reliability Modeling for Smart Grids
- Case Studies on Power Grid Failures and Lessons Learned
- Application of Reduced Complexity Models in Real-World Systems
Future Directions
- Emerging Trends in Power Grid Reliability Modeling
- Potential for Machine Learning and AI in Grid Reliability
- Challenges and Opportunities for Future Research
Conclusion
- Summary of Key Findings
- The Impact of Complexity Reduction on Power Grid Reliability
- Final Thoughts on Improving Power Grid Resilience
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