Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization
Icing is one of the most serious threats to power grid security in cold seasons. This article studies a problem of cooperatively scheduling inspection unmanned aerial vehicles (UAVs) and deicing UAVs for power grid deicing, the aim of which is to minimize the total expected loss of outages and colla...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Industry Applications |
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| Online Access: | https://ieeexplore.ieee.org/document/10815062/ |
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| author | Yu-Jun Zheng Zhi-Yuan Zhang Jia-Yu Yan Wei-Guo Sheng |
| author_facet | Yu-Jun Zheng Zhi-Yuan Zhang Jia-Yu Yan Wei-Guo Sheng |
| author_sort | Yu-Jun Zheng |
| collection | DOAJ |
| description | Icing is one of the most serious threats to power grid security in cold seasons. This article studies a problem of cooperatively scheduling inspection unmanned aerial vehicles (UAVs) and deicing UAVs for power grid deicing, the aim of which is to minimize the total expected loss of outages and collapses caused by the icing disaster. Uncertain outage risk, collapse risk, and deicing workload of each power line are modeled as fuzzy values predicted by fuzzy deep learning models, and we transform the fuzzy optimization problem into a crisp optimization problem based on fuzzy arithmetics and uncertain theory. We propose an evolutionary algorithm, which combines global search without individual interaction and adaptive local search that uses a fuzzy inference system to determine the operator to be applied on each solution. The algorithm is fully parallelizable and therefore can solve the problem very efficiently based on GPU parallel acceleration. Computational results on real-world problem instances validate the performance of the proposed method compared to the state of the arts. |
| format | Article |
| id | doaj-art-bdac753dc94448998dde4a04ee00e790 |
| institution | Kabale University |
| issn | 2644-1241 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Industry Applications |
| spelling | doaj-art-bdac753dc94448998dde4a04ee00e7902025-01-14T00:02:57ZengIEEEIEEE Open Journal of Industry Applications2644-12412025-01-016153310.1109/OJIA.2024.352207210815062Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary OptimizationYu-Jun Zheng0https://orcid.org/0000-0002-6095-6325Zhi-Yuan Zhang1Jia-Yu Yan2Wei-Guo Sheng3School of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaIcing is one of the most serious threats to power grid security in cold seasons. This article studies a problem of cooperatively scheduling inspection unmanned aerial vehicles (UAVs) and deicing UAVs for power grid deicing, the aim of which is to minimize the total expected loss of outages and collapses caused by the icing disaster. Uncertain outage risk, collapse risk, and deicing workload of each power line are modeled as fuzzy values predicted by fuzzy deep learning models, and we transform the fuzzy optimization problem into a crisp optimization problem based on fuzzy arithmetics and uncertain theory. We propose an evolutionary algorithm, which combines global search without individual interaction and adaptive local search that uses a fuzzy inference system to determine the operator to be applied on each solution. The algorithm is fully parallelizable and therefore can solve the problem very efficiently based on GPU parallel acceleration. Computational results on real-world problem instances validate the performance of the proposed method compared to the state of the arts.https://ieeexplore.ieee.org/document/10815062/Cooperative unmanned aerial vehicles (UAVs) schedulingevolutionary optimizationfuzzy learningfuzzy optimizationpower grid deicing |
| spellingShingle | Yu-Jun Zheng Zhi-Yuan Zhang Jia-Yu Yan Wei-Guo Sheng Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization IEEE Open Journal of Industry Applications Cooperative unmanned aerial vehicles (UAVs) scheduling evolutionary optimization fuzzy learning fuzzy optimization power grid deicing |
| title | Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization |
| title_full | Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization |
| title_fullStr | Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization |
| title_full_unstemmed | Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization |
| title_short | Cooperative UAV Scheduling for Power Grid Deicing Using Fuzzy Learning and Evolutionary Optimization |
| title_sort | cooperative uav scheduling for power grid deicing using fuzzy learning and evolutionary optimization |
| topic | Cooperative unmanned aerial vehicles (UAVs) scheduling evolutionary optimization fuzzy learning fuzzy optimization power grid deicing |
| url | https://ieeexplore.ieee.org/document/10815062/ |
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