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Machine learning-assisted outage planning for maintenance activities in power systems with renewables

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  • Toubeau, Jean-François
  • Pardoen, Lorie
  • Hubert, Louis
  • Marenne, Nicolas
  • Sprooten, Jonathan
  • De Grève, Zacharie
  • Vallée, François

Abstract

The optimal coordination of maintenances is becoming increasingly important to guarantee the security of supply in renewable-dominated power systems. However, current planning tools are plagued with tractability issues arising from the need to comply with operational security standards. The grid must indeed safely accommodate any unexpected contingency occurring during the scheduled maintenances, which requires simulating many different scenarios. To alleviate this computational burden, this paper proposes to leverage machine learning models to predict the outcome of contingency analyses in a fast and reliable manner. The methodology is tested on the full regional transmission grid of Belgium, covering the voltage levels from 150 kV down to 30 kV. Different models, including naive Bayes classifiers, support vector machines and tree-based models, are tested and compared. Outcomes reveal that random forests consistently outperform other benchmarks, by identifying with an accuracy higher than 90% the time periods during which maintenances can be safely performed. Also, we show that the expected rise in renewable generation will impact the maintainability of the future system, with an increase of up to 20% of unsuitable periods to perform maintenances in some grid areas.

Suggested Citation

  • Toubeau, Jean-François & Pardoen, Lorie & Hubert, Louis & Marenne, Nicolas & Sprooten, Jonathan & De Grève, Zacharie & Vallée, François, 2022. "Machine learning-assisted outage planning for maintenance activities in power systems with renewables," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022416
    DOI: 10.1016/j.energy.2021.121993
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    References listed on IDEAS

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    1. Shayesteh, E. & Yu, J. & Hilber, P., 2018. "Maintenance optimization of power systems with renewable energy sources integrated," Energy, Elsevier, vol. 149(C), pages 577-586.
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    Cited by:

    1. Hanyu Gu & Hue Chi Lam & Thi Thanh Thu Pham & Yakov Zinder, 2023. "Heuristics and meta-heuristic to solve the ROADEF/EURO challenge 2020 maintenance planning problem," Journal of Heuristics, Springer, vol. 29(1), pages 139-175, February.
    2. Fouzi Harrou & Bilal Taghezouit & Sofiane Khadraoui & Abdelkader Dairi & Ying Sun & Amar Hadj Arab, 2022. "Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems," Energies, MDPI, vol. 15(18), pages 1-28, September.
    3. Bakhshideh Zad, Bashir & Toubeau, Jean-François & Bruninx, Kenneth & Vatandoust, Behzad & De Grève, Zacharie & Vallée, François, 2022. "Supervised learning-assisted modeling of flow-based domains in European resource adequacy assessments," Applied Energy, Elsevier, vol. 325(C).
    4. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    5. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.

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