Linear Single- and Three-Phase Voltage Forecasting and Bayesian State Estimation With Limited Sensing
Date Published |
11/2019
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Publication Type | Journal Article
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Authors | |
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DOI |
10.1109/TPWRS.2019.2955893
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Abstract |
Implementing state estimation in low and mediumvoltage power distribution is still challenging given the scale of many networks and the reliance of traditional methods on a large number of measurements. This paper proposes a method to improve voltage predictions in real-time by leveraging a limited set of real-time measurements. The method relies on Bayesian estimation formulated as a linear least squares estimation problem, which resembles the classical weighted least-squares (WLS) approach for scenarios where full network observability is not available. We build on recently developed linear approximations for unbalanced three-phase power flow to construct voltage predictions as a linear mapping of load predictions constructed with Gaussian processes. The estimation step to update the voltage forecasts in real-time is a linear computation allowing fast high-resolution state estimate updates. The uncertainty in forecasts can be determined a priori and smoothed a posteriori, making the method useful for both planning, operation and post-hoc analysis. The method outperforms conventional WLS and is applied to different test feeders and validated on a real test feeder with the utility Alliander in The Netherlands. |
Journal |
IEEE Transactions on Power Systems
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Volume |
35
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Year of Publication |
2020
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Issue |
3
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Pagination |
1674 - 1683
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ISSN Number |
0885-8950
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Short Title |
IEEE Trans. Power Syst.
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Refereed Designation |
Refereed
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Organizations | |
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