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Advances in Electric Power and Energy
Читать книгу Advances in Electric Power and Energy - Группа авторов - Страница 1
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Страница 1
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
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ABOUT THE EDITOR
Страница 12
ABOUT THE CONTRIBUTORS
CHAPTER
1
GENERAL CONSIDERATIONS
1.1 PRELUDE
1.2 DEFINING SSE
1.3 THE NEED FOR STATE ESTIMATION
1.4 STATIC STATE ESTIMATION IN PRACTICE
1.4.1 SE Performance Issues
1.4.2 Weights Assigned to Measurements
1.4.3 SE Availability Considerations
1.4.4 SE Solution Quality (Accuracy)
1.4.4.1 Metrics to Evaluate SE Solution Quality
1.4.4.2 Methods for Evaluating SE Solution Quality (Accuracy)
1.4.5 Using SE to Monitor External Facilities
1.4.6 SE Maintenance/Troubleshooting and Support Practices
1.5 APPLICATIONS THAT USE SE SOLUTION
1.5.1 Contingency Analysis
1.5.2 Power Flow (Online/Operator)
1.5.3 Locational Marginal Pricing
1.5.4 Security‐Constrained Economic Dispatch
1.5.5 Voltage Stability Assessment
1.5.6 Dynamic Stability Assessment
1.6 OVERVIEW OF CHAPTERS
REFERENCES
CHAPTER
2
STATE ESTIMATION IN POWER SYSTEMS BASED ON A MATHEMATICAL PROGRAMMING APPROACH
2.1 INTRODUCTION
2.2 FORMULATION
Example 2.1 Traditional Formulation
2.3 CLASSICAL STATE ESTIMATION PROCEDURE
Example 2.2 Classical Solution Example
2.3.1 Bad Measurement Detection
Example 2.3 Bad Measurement Detection Example
2.3.2 Identification of Erroneous Measurements
Example 2.4 Bad Measurement Identification Example
2.4 MATHEMATICAL PROGRAMMING SOLUTION
Example 2.5 Mathematical Programming Problem
2.5 ALTERNATIVE STATE ESTIMATORS
2.5.1 Weighted Least of Squares
2.5.1.1 WLS General Formulation
2.5.2 Weighted Least Absolute Value
2.5.2.1 LAV General Formulation
2.5.2.2 LAV Mathematical Programming Formulation
2.5.3 Quadratic‐Constant Criterion
2.5.3.1 QC General Formulation
2.5.3.2 QC Mathematical Programming Formulation
2.5.4 Quadratic‐Linear Criterion
2.5.4.1 QL General Formulation
2.5.4.2 QL Mathematical Programming Formulation
2.5.5 Least Median of Squares
2.5.5.1 LMS General Formulation
2.5.5.2 LMS Mathematical Programming Formulation
2.5.6 Least Trimmed of Squares
2.5.6.1 LTS General Formulation
2.5.6.2 LTS Mathematical Programming Formulation
2.5.7 Least Measurements Rejected
2.5.7.1 LMR General Formulation
2.5.7.2 LMR Mathematical Programming Formulation
2.5.8 Formulation Overview
2.5.9 Illustrative Example
2.5.10 Case Study
2.5.10.1 Estimation Assessment
2.5.11 Results
2.5.11.1 Performance Analysis: Bad Data
2.5.12 Conclusions
REFERENCES
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