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- W3112157041 abstract "This chapter aspires to glean some of the recent advances in power system state estimation (PSSE). The Cramér–Rao bound, a lower bound on the (co)variance of any unbiased estimator, is first derived for the PSSE setup. After reviewing the classical Gauss–Newton iterations, contemporary PSSE solvers leveraging relaxations to convex programs and successive convex approximations are explored. Dynamic PSSE is well motivated thanks to its improved robustness, observability, and predictive ability when additional temporal information is available. A disciplined paradigm for distributed and decentralized schemes is subsequently exemplified under linear(ized) and exact grid models. Novel bad data processing models and fresh perspectives linking critical measurements to cyberattacks on the state estimator are presented. Finally, spurred by advances in online convex optimization (OCO), model-free and model-based state trackers are reviewed." @default.
- W3112157041 created "2020-12-21" @default.
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- W3112157041 date "2020-12-10" @default.
- W3112157041 modified "2023-10-16" @default.
- W3112157041 title "PSSE REDUX" @default.
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- W3112157041 doi "https://doi.org/10.1002/9781119480402.ch6" @default.
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