Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- Part II Data-Driven Anomaly Detection
- Part III Data Quality, Integrity, and Privacy
- Part IV Signal Processing
- 12 A Data Analytics Perspective of Fundamental Power Grid Analysis Techniques
- 13 Graph Signal Processing for the Power Grid
- 14 A Sparse Representation Approach for Anomaly Identification
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
14 - A Sparse Representation Approach for Anomaly Identification
from Part IV - Signal Processing
Published online by Cambridge University Press: 22 March 2021
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- Part II Data-Driven Anomaly Detection
- Part III Data Quality, Integrity, and Privacy
- Part IV Signal Processing
- 12 A Data Analytics Perspective of Fundamental Power Grid Analysis Techniques
- 13 Graph Signal Processing for the Power Grid
- 14 A Sparse Representation Approach for Anomaly Identification
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
Summary
Fast and accurate unveiling of power-line outages is of paramount importance not only for preventing faults that may lead to blackouts but also for routine monitoring and control tasks of the smart grid. This chapter presents a sparse overcomplete model to represent the effects of (potentially multiple) power line outages on synchronized bus voltage angle measurements. Based on this model, efficient compressive sensing algorithms can be adopted to identify outaged lines at linear complexity of the total number of lines. Furthermore, the effects of uncertainty in synchronized measurements will be analyzed, along with the optimal placement of measurement units.
- Type
- Chapter
- Information
- Advanced Data Analytics for Power Systems , pp. 340 - 360Publisher: Cambridge University PressPrint publication year: 2021