Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- Part II Data-Driven Anomaly Detection
- 5 Quickest Detection and Isolation of Transmission Line Outages
- 6 Active Sensing for Quickest Anomaly Detection
- 7 Random Matrix Theory for Analyzing Spatio-Temporal Data
- 8 Graph-Theoretic Analysis of Power Grid Robustness
- Part III Data Quality, Integrity, and Privacy
- Part IV Signal Processing
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
7 - Random Matrix Theory for Analyzing Spatio-Temporal Data
from Part II - Data-Driven Anomaly Detection
Published online by Cambridge University Press: 22 March 2021
Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- Part II Data-Driven Anomaly Detection
- 5 Quickest Detection and Isolation of Transmission Line Outages
- 6 Active Sensing for Quickest Anomaly Detection
- 7 Random Matrix Theory for Analyzing Spatio-Temporal Data
- 8 Graph-Theoretic Analysis of Power Grid Robustness
- Part III Data Quality, Integrity, and Privacy
- Part IV Signal Processing
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
Summary
This chapter introduces the fundamental elements of random matrix theory and highlights key applications in line outage detection using actual data recovered from existing power systems around the globe. The key mathematical component is a novel concept referred to as the mean spectral radius (MSR) of non-Hermitian random matrices. By analyzing the changes of the MSR of random matrices, grid failure detection is reliably achieved. Several studies and simulations are considered to observe the performance of this new theoretical approach to line outage detection.
- Type
- Chapter
- Information
- Advanced Data Analytics for Power Systems , pp. 144 - 174Publisher: Cambridge University PressPrint publication year: 2021