Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- 1 Learning Power Grid Topologies
- 2 Probabilistic Forecasting of Power System and Market Operations
- 3 Deep Learning in Power Systems
- 4 Estimating the System State and Network Model Errors
- Part II Data-Driven Anomaly Detection
- Part III Data Quality, Integrity, and Privacy
- Part IV Signal Processing
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
4 - Estimating the System State and Network Model Errors
from Part I - Statistical Learning
Published online by Cambridge University Press: 22 March 2021
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- 1 Learning Power Grid Topologies
- 2 Probabilistic Forecasting of Power System and Market Operations
- 3 Deep Learning in Power Systems
- 4 Estimating the System State and Network Model Errors
- Part II Data-Driven Anomaly Detection
- Part III Data Quality, Integrity, and Privacy
- Part IV Signal Processing
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
Summary
This chapter reviews the methods used to estimate the state of a power system and its network model based on the measurements provided by supervisory control and data acquisition (SCADA) systems and/or by phasor measurement units (PMU). Initially, it provides an overview of the commonly implemented SCADA-based state estimators. Network observability and bad data processing functions are briefly described. This is followed by a description of the changes in the problem formulation and solution introduced by the incorporation of PMU measurements as well as the associated opportunities and challenges. Finally, detection, identification, and correction of network model errors and impact of such errors on system reliability as well as market operations are presented.
- Type
- Chapter
- Information
- Advanced Data Analytics for Power Systems , pp. 74 - 98Publisher: Cambridge University PressPrint publication year: 2021