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A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

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  • John O’Donnell

    (DTE Electric, Detroit, MI 48226, USA
    Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

Abstract

New technologies, such as electric vehicles, rooftop solar, and behind-the-meter storage, will lead to increased variation in electrical load, and the location and time of the penetration of these technologies are uncertain. Power quality, reliability, and protection issues can be the result if electric utilities do not consider the probability of load scenarios that have not yet occurred. The authors’ approach to addressing these concerns started with collecting the electrical load data for an expansive and diverse set of distribution transformers. This provided approximately two-and-a-half years of data that were used to develop new methods that will enable engineers to address emerging issues. The efficacy of the methods was then assessed with a real-world test dataset that was not used in the development of the new methods. This resulted in an approach to efficiently generate stochastic electrical load forecasts for elements of distribution circuits. Methods are also described that use those forecasts for engineering analysis that predict the likelihood of distribution transformer failures and power quality events. 100% of the transformers identified as most likely to fail either did fail or identified a data correction opportunity. The accuracy of the power quality results was 92% while allowing for a balance between measures of efficiency and customer satisfaction.

Suggested Citation

  • John O’Donnell & Wencong Su, 2023. "A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities," Energies, MDPI, vol. 16(21), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7251-:d:1267245
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    References listed on IDEAS

    as
    1. John O’Donnell & Wencong Su, 2023. "Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System," Energies, MDPI, vol. 16(15), pages 1-21, July.
    2. Spyros Giannelos & Stefan Borozan & Marko Aunedi & Xi Zhang & Hossein Ameli & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2023. "Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids," Energies, MDPI, vol. 16(13), pages 1-15, June.
    3. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    4. Jiakang Wang & Hui Liu & Guangji Zheng & Ye Li & Shi Yin, 2023. "Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-16, May.
    5. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    Full references (including those not matched with items on IDEAS)

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