Optimizing generating unit maintenance with the league championship method: A reliability-based approach
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Optimizing generating unit maintenance with the league championship method: A reliability-based approach
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| Автор |
Gubin, P. Y.
Kamel, S. Safaraliev, M. Senyuk, M. Hussien, A. G. Zawbaa, H. M. |
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| Тематика |
DIFFERENTIAL EVOLUTION METHOD
DIRECTED SEARCH METHOD EXPECTED DEMAND NOT SUPPLIED EXPECTED ENERGY NOT SUPPLIED GENERATING ADEQUACY GENERATION MAINTENANCE SCHEDULING LEAGUE CHAMPIONSHIP ALGORITHM MONTE-CARLO METHOD PARTICLE SWARM METHOD POWER SYSTEM CONDITION BASED MAINTENANCE EVOLUTIONARY ALGORITHMS HEURISTIC METHODS OPTIMIZATION DIFFERENTIAL EVOLUTION METHOD DIRECTED SEARCH METHOD DIRECTED SEARCHES EXPECTED DEMAND NOT SUPPLIED EXPECTED ENERGY NOT SUPPLIED GENERATING ADEQUACY GENERATION MAINTENANCE SCHEDULING LEAGUE CHAMPIONSHIP ALGORITHMS MONTECARLO METHODS PARTICLE SWARM METHODS POWER POWER SYSTEM SEARCH METHOD MONTE CARLO METHODS |
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| Описание |
The electrical power industry has experienced an unprecedented pace of digital transformation as a prevailing economic trend in recent years. This shift towards digitalization has resulted in an increasing interest in the collection of real-time equipment condition data, which provides opportunities for implementing sensor-driven condition-based repair. As a result, there is a growing need for the development of generator maintenance scheduling to consider probabilistic equipment behavior, which requires significant computational efforts. To address this issue, the research proposes the use of a meta-heuristic league championship method (LCM) for generator maintenance scheduling, considering random generation profiles based on generation adequacy criteria. The experimental part of the study compares this approach and its modifications to widely used meta-heuristics, such as differential evolution and particle swarm methods. The identification and demonstration of optimal method settings for the generation maintenance scheduling problem are presented. Subsequently, it is illustrated that employing random league scheduling expedience can reduce the variance of objective function values in resulting plans by over three times, with values of 0.632 MWh and 0.205 MWh for conventional and proposed techniques respectively. In addition, three approaches are compared to assess generation adequacy corresponding to different schedules. The study emphasizes the efficacy of employing the LCM approach in scheduling generator maintenance. Specifically, it showcases that among all the methods examined, the LCM approach exhibits the lowest variance in objective function values, with values of 38.81 and 39.90 MWh for LCM and its closest rival, the modified particle swarm method (MPSM), respectively. © 2023 The Author(s)
The authors are very thankful to the anonymous reviewers for helping in improving the paper through their observations and suggestions. |
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| Дата |
2024-04-05T16:27:12Z
2024-04-05T16:27:12Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Gubin, PY, Kamel, S, Safaraliev, M, Senyuk, M, Hussien, AG & Zawbaa, HM 2023, 'Optimizing generating unit maintenance with the league championship method: A reliability-based approach', Energy Reports, Том. 10, стр. 135-152. https://doi.org/10.1016/j.egyr.2023.06.024
Gubin, P. Y., Kamel, S., Safaraliev, M., Senyuk, M., Hussien, A. G., & Zawbaa, H. M. (2023). Optimizing generating unit maintenance with the league championship method: A reliability-based approach. Energy Reports, 10, 135-152. https://doi.org/10.1016/j.egyr.2023.06.024 2352-4847 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163888026&doi=10.1016%2fj.egyr.2023.06.024&partnerID=40&md5=c15c93dba7a94c7d52496d76631b0f27 https://doi.org/10.1016/j.egyr.2023.06.024 http://elar.urfu.ru/handle/10995/130607 10.1016/j.egyr.2023.06.024 85163888026 001034336400001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Elsevier Ltd
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| Источник |
Energy Reports
Energy Reports |
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