Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation

Lu, Junyi and Stephen, Bruce and Brown, Blair (2024) Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation. Data-Centric Engineering. ISSN 2632-6736 (In Press) (https://doi.org/10.1017/dce.2024.10)

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Abstract

Traditionally, electricity distribution networks were designed for unidirectional power flow without the need to accommodate generation installed at the point of use. However, with the increase in Distributed Energy Resources and other Low Carbon Technologies, the role of distribution networks is changing. This shift brings challenges, including the need for intensive metering and more frequent reconfiguration to identify threats from voltage and thermal violations. Mitigating action through reconfiguration is informed by State Estimation which is especially challenging for LV distribution networks where the constraints of low observability, non-linear load relationships and highly unbalanced systems all contribute to the difficulty of producing accurate state estimates. To counter low observability, this paper proposes the application of a novel transfer learning methodology, based upon the concept of Conditional Online Bayesian Transfer, to make forward predictions of bus pseudo-measurements. Day ahead loads forecasts at a fully observed point on the network are adjusted using the intraday residuals at other points in the network to provide them with load forecasts without the need for a complete set of forecast models at all substations. These form pseudo-measurements which then inform the state estimates at future time points. This methodology is demonstrated on both a representative IEEE Test network and on an actual GB 11kV feeder network.

  • Item type:Article
    ID code:88952
    Dates:
    Date
    Event
    7 April 2024
    Published
    7 April 2024
    Accepted
    Subjects:UNSPECIFIED
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:25 Apr 2024 08:48
    Last modified:25 Apr 2024 08:48
    URI:https://strathprints.strath.ac.uk/id/eprint/88952
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