Perspective Chapter: Robust State Estimation for Future Power Systems – Overcoming the Drawbacks of Traditional State Estimators
<p id="p1">This chapter presents a comprehensive examination of the limitations of traditional power system state estimation methods, particularly Weighted Least Squares (WLS) and Least Absolute Value (LAV), in the face of growing system complexity, measurement inaccuracies, and stressed operating c...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2025
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1229049 |
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| Sumario: | <p id="p1">This chapter presents a comprehensive examination of the limitations of traditional power system state estimation methods, particularly Weighted Least Squares (WLS) and Least Absolute Value (LAV), in the face of growing system complexity, measurement inaccuracies, and stressed operating conditions. Traditional state estimators, originally developed under assumptions of linearity, Gaussian noise, and steady-state operation, increasingly find it difficult to deliver accurate and robust results in modern grids characterized by higher and higher renewable integration, and high-frequency disturbances, including extreme weather events. Through illustrative examples, including a small system case study, this chapter highlights critical issues such as convergence failures, susceptibility to leverage points, and the pervasive problem of negative loads. A new class of estimators is briefly introduced, emphasizing selective measurement use, sparse recovery, and robustness to bad data. Simulation results on large-scale and real-world utility systems demonstrate the superior accuracy, convergence reliability, and bad data rejection capabilities of this new class state estimator. The chapter concludes by identifying key directions for future research in distributed, dynamic, and AI-augmented state estimation, underscoring the need for resilient algorithms in next-generation energy management systems.</p> |
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| ISBN: | 978-1-83635-686-8 |
| DOI: | 10.5772/intechopen.1012247 |