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- W3164725986 abstract "This chapter aims to provide an introduction to data-driven model-based state estimators for real-time monitoring of the power grid, highlighting the structure and motivation that led the authors to accomplish it by organizing the following sections: Section 1.1 Data-driven models describe the value of the data-driven state estimation solutions considering temporal and spatial characteristics for real-time monitoring of electric power systems (EPSs). Concepts and the general framework of this process are introduced, contextualizing the development of data-driven state estimators, and the need for evolution to keep in line with the new trends and current technologies. Data-driven linear regression models are introduced. Section 1.2 The Ensemble CorrDet with Adaptive Statistics (ECD-AS) algorithm is presented. The Ensemble CorrDet (ECD) algorithm is initially introduced. The ECD algorithm described uses fixed estimated mean and covariance of normal samples after training, rather than adapting to the changing state of power systems. This means that when applied to a realistic load profile for power systems, ECD statistics will quickly become outdated and the performance of the algorithm will decrease. Thus, there is a need for new algorithms that can adapt to the real-time environment. An enhanced, adaptive, and responsive data-driven method for gross error analytics, called Ensemble CorrDet with Adaptive Statistics (ECD-AS), that accounts for the rapidly changing power system state is introduced." @default.
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- W3164725986 date "2021-01-01" @default.
- W3164725986 modified "2023-09-23" @default.
- W3164725986 title "Data-driven state estimation in electric power systems" @default.
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- W3164725986 doi "https://doi.org/10.1016/b978-0-323-90033-1.00003-2" @default.
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