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• W2808150771 abstract "State Estimation is an essential technique to provide observability in power systems. Traditionally developed for high-voltage transmission networks, state estimation requires equipping networks with many real-time sensors, which remains a challenge at the scale of distribution networks. This paper proposes a method to complement a limited set of real-time measurements with voltage predictions from forecast models. The method differs from the classical weighted least-squares approach, and instead relies on Bayesian estimation formulated as a linear least squares estimation problem. We integrate recently developed linear models for unbalanced 3-phase power flow to construct voltage predictions as a linear mapping of load predictions. The estimation step is a linear computation allowing high resolution state estimate updates, for instance by exploiting a small set of phasor measurement units. Uncertainties can be determined a priori and smoothed a posteriori, making the method useful for both planning, operation and post hoc analysis. The method is applied to an IEEE benchmark and on a real network testbed at the Dutch utility Alliander." @default.
• W2808150771 created "2018-06-21" @default.
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• W2808150771 date "2018-06-15" @default.
• W2808150771 modified "2023-09-27" @default.
• W2808150771 title "Forecasting-Based State Estimation for Three-Phase Distribution Systems with Limited Sensing." @default.
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• W2808150771 hasPublicationYear "2018" @default.
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