A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement
The number and location of switching devices (e.g., circuit breakers and sectionalizers) should be optimally determined in power distribution systems to reduce system interruptions and associated costs. However, existing mathematical optimization algorithms, such as classic and metaheuristic methods...
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University of Isfahan
2024-09-01
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| Series: | هوش محاسباتی در مهندسی برق |
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| Online Access: | https://isee.ui.ac.ir/article_28834_b29ab26184c4c64cd9697d883d0ea1fe.pdf |
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| author | Mehrdad Ebrahimi Mohammad Rastegar |
| author_facet | Mehrdad Ebrahimi Mohammad Rastegar |
| author_sort | Mehrdad Ebrahimi |
| collection | DOAJ |
| description | The number and location of switching devices (e.g., circuit breakers and sectionalizers) should be optimally determined in power distribution systems to reduce system interruptions and associated costs. However, existing mathematical optimization algorithms, such as classic and metaheuristic methods, cannot solve the optimal switch placement problem for large-scale systems. In this paper, a scalable model is proposed based on machine learning methods to determine the optimal number and location of switching devices according to system conditions. This paper proposes employing ensemble learning methods and explainable artificial intelligence tools to build an accurate data-driven model. Consequently, power distribution operators can determine the optimal number and location of circuit breakers, remote-controlled sectionalizers, and manual switches in large-scale systems without mathematical optimization algorithms. To validate its accuracy and scalability, the proposed model and a classic-based model are implemented on a real power distribution system in Fars province. The numerical results demonstrate that the proposed data-driven model can find a solution close to the globally optimal solution quickly, using a limited range of system data. |
| format | Article |
| id | doaj-art-daa2acf817d6412cb4f46001b07c250a |
| institution | Kabale University |
| issn | 2821-0689 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | University of Isfahan |
| record_format | Article |
| series | هوش محاسباتی در مهندسی برق |
| spelling | doaj-art-daa2acf817d6412cb4f46001b07c250a2025-01-26T07:58:37ZengUniversity of Isfahanهوش محاسباتی در مهندسی برق2821-06892024-09-0115311410.22108/isee.2024.141003.168228834A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability ImprovementMehrdad Ebrahimi0Mohammad Rastegar1Ph.D., Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, IranAssociate Prof., Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, IranThe number and location of switching devices (e.g., circuit breakers and sectionalizers) should be optimally determined in power distribution systems to reduce system interruptions and associated costs. However, existing mathematical optimization algorithms, such as classic and metaheuristic methods, cannot solve the optimal switch placement problem for large-scale systems. In this paper, a scalable model is proposed based on machine learning methods to determine the optimal number and location of switching devices according to system conditions. This paper proposes employing ensemble learning methods and explainable artificial intelligence tools to build an accurate data-driven model. Consequently, power distribution operators can determine the optimal number and location of circuit breakers, remote-controlled sectionalizers, and manual switches in large-scale systems without mathematical optimization algorithms. To validate its accuracy and scalability, the proposed model and a classic-based model are implemented on a real power distribution system in Fars province. The numerical results demonstrate that the proposed data-driven model can find a solution close to the globally optimal solution quickly, using a limited range of system data.https://isee.ui.ac.ir/article_28834_b29ab26184c4c64cd9697d883d0ea1fe.pdfmachine learningoptimal switch placementoptimizationpower distribution systemsprotective devicesremote-controlled switch |
| spellingShingle | Mehrdad Ebrahimi Mohammad Rastegar A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement هوش محاسباتی در مهندسی برق machine learning optimal switch placement optimization power distribution systems protective devices remote-controlled switch |
| title | A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement |
| title_full | A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement |
| title_fullStr | A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement |
| title_full_unstemmed | A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement |
| title_short | A Scalable Ensemble Learning-Based Model for Optimal Placement of Circuit Breaker and Sectionalizer in Power Distribution Systems with the Aim of Reliability Improvement |
| title_sort | scalable ensemble learning based model for optimal placement of circuit breaker and sectionalizer in power distribution systems with the aim of reliability improvement |
| topic | machine learning optimal switch placement optimization power distribution systems protective devices remote-controlled switch |
| url | https://isee.ui.ac.ir/article_28834_b29ab26184c4c64cd9697d883d0ea1fe.pdf |
| work_keys_str_mv | AT mehrdadebrahimi ascalableensemblelearningbasedmodelforoptimalplacementofcircuitbreakerandsectionalizerinpowerdistributionsystemswiththeaimofreliabilityimprovement AT mohammadrastegar ascalableensemblelearningbasedmodelforoptimalplacementofcircuitbreakerandsectionalizerinpowerdistributionsystemswiththeaimofreliabilityimprovement AT mehrdadebrahimi scalableensemblelearningbasedmodelforoptimalplacementofcircuitbreakerandsectionalizerinpowerdistributionsystemswiththeaimofreliabilityimprovement AT mohammadrastegar scalableensemblelearningbasedmodelforoptimalplacementofcircuitbreakerandsectionalizerinpowerdistributionsystemswiththeaimofreliabilityimprovement |