Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems
ABSTRACT Modern power systems encounter significant challenges in optimal power flow (OPF) management due to the unpredictable nature of wind energy integration. Flexible AC Transmission System (FACTS) devices, including Static VAR Compensator (SVC), Thyristor‐Controlled Series Compensator (TCSC), a...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70167 |
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| author | Sunilkumar P. Agrawal Pradeep Jangir Arpita Sundaram B. Pandya Anil Parmar Mohammad Khishe Bhargavi Indrajit Trivedi |
| author_facet | Sunilkumar P. Agrawal Pradeep Jangir Arpita Sundaram B. Pandya Anil Parmar Mohammad Khishe Bhargavi Indrajit Trivedi |
| author_sort | Sunilkumar P. Agrawal |
| collection | DOAJ |
| description | ABSTRACT Modern power systems encounter significant challenges in optimal power flow (OPF) management due to the unpredictable nature of wind energy integration. Flexible AC Transmission System (FACTS) devices, including Static VAR Compensator (SVC), Thyristor‐Controlled Series Compensator (TCSC), and Thyristor‐Controlled Phase Shifter (TCPS), enhance system stability, reduce losses, and lower operational costs when optimally placed. Conventional optimization techniques like Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) struggle to balance exploration and exploitation in complex OPF problems, leading to suboptimal solutions. This study proposes a novel hybrid metaheuristic approach, the Orthogonal Multi‐swarm Greedy Selection Sine Cosine Algorithm (OMGSCA), integrating orthogonal learning, multi‐swarm mechanisms, and greedy selection to enhance solution quality. Orthogonal learning explores new search spaces, while the multi‐swarm strategy improves exploitation. The greedy selection mechanism prevents premature convergence. OMGSCA optimizes FACTS device placement and sizing in wind‐integrated power systems under fixed and uncertain loading conditions. Performance evaluation on the IEEE 30‐bus test system with wind energy and FACTS devices demonstrates OMGSCA's superiority over traditional algorithms. Case studies focus on minimizing generation costs, active power losses, and gross costs. Results show OMGSCA achieves a power loss of 5.6209 MW in Case 1, comparable to WOA (5.6121 MW) and outperforming PSO, SCA, and MFO by 0.90%, 0.06%, and 0.57%, respectively. OMGSCA's gross generation cost (1369.3961 $/h) surpasses PSO, SCA, MFO, and GWO by 0.39%, 0.28%, 3.48%, and 0.20%, respectively. The algorithm proves effective in OPF problems, delivering cost‐efficient operations, reduced losses, and enhanced stability across varying load conditions. |
| format | Article |
| id | doaj-art-3ca0efe248d344cbba5d84aef56026bc |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-3ca0efe248d344cbba5d84aef56026bc2025-08-20T03:48:27ZengWileyEngineering Reports2577-81962025-05-0175n/an/a10.1002/eng2.70167Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power SystemsSunilkumar P. Agrawal0Pradeep Jangir1Arpita2Sundaram B. Pandya3Anil Parmar4Mohammad Khishe5Bhargavi Indrajit Trivedi6Department of Electrical Engineering Government Engineering College Gandhinagar IndiaUniversity Centre for Research and Development Chandigarh University Gharuan IndiaDepartment of Biosciences Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences Chennai IndiaDepartment of Electrical Engineering Shri K.J. Polytechnic Bharuch IndiaDepartment of Electrical Engineering Shri K.J. Polytechnic Bharuch IndiaDepartment of Electrical Engineering Imam Khomeini Naval Science University of Nowshahr Nowshahr IranVishwakarma Government Engineering College Ahmedabad IndiaABSTRACT Modern power systems encounter significant challenges in optimal power flow (OPF) management due to the unpredictable nature of wind energy integration. Flexible AC Transmission System (FACTS) devices, including Static VAR Compensator (SVC), Thyristor‐Controlled Series Compensator (TCSC), and Thyristor‐Controlled Phase Shifter (TCPS), enhance system stability, reduce losses, and lower operational costs when optimally placed. Conventional optimization techniques like Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) struggle to balance exploration and exploitation in complex OPF problems, leading to suboptimal solutions. This study proposes a novel hybrid metaheuristic approach, the Orthogonal Multi‐swarm Greedy Selection Sine Cosine Algorithm (OMGSCA), integrating orthogonal learning, multi‐swarm mechanisms, and greedy selection to enhance solution quality. Orthogonal learning explores new search spaces, while the multi‐swarm strategy improves exploitation. The greedy selection mechanism prevents premature convergence. OMGSCA optimizes FACTS device placement and sizing in wind‐integrated power systems under fixed and uncertain loading conditions. Performance evaluation on the IEEE 30‐bus test system with wind energy and FACTS devices demonstrates OMGSCA's superiority over traditional algorithms. Case studies focus on minimizing generation costs, active power losses, and gross costs. Results show OMGSCA achieves a power loss of 5.6209 MW in Case 1, comparable to WOA (5.6121 MW) and outperforming PSO, SCA, and MFO by 0.90%, 0.06%, and 0.57%, respectively. OMGSCA's gross generation cost (1369.3961 $/h) surpasses PSO, SCA, MFO, and GWO by 0.39%, 0.28%, 3.48%, and 0.20%, respectively. The algorithm proves effective in OPF problems, delivering cost‐efficient operations, reduced losses, and enhanced stability across varying load conditions.https://doi.org/10.1002/eng2.70167FACTS devicesoptimal power flow (OPF)orthogonal multi‐swarm greedy selection sine cosine algorithm (OMGSCA)sine cosine algorithm (SCA)wind integration |
| spellingShingle | Sunilkumar P. Agrawal Pradeep Jangir Arpita Sundaram B. Pandya Anil Parmar Mohammad Khishe Bhargavi Indrajit Trivedi Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems Engineering Reports FACTS devices optimal power flow (OPF) orthogonal multi‐swarm greedy selection sine cosine algorithm (OMGSCA) sine cosine algorithm (SCA) wind integration |
| title | Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems |
| title_full | Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems |
| title_fullStr | Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems |
| title_full_unstemmed | Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems |
| title_short | Orthogonal Multi‐Swarm Greedy Selection Based Sine Cosine Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems |
| title_sort | orthogonal multi swarm greedy selection based sine cosine algorithm for optimal facts placement in uncertain wind integrated scenario based power systems |
| topic | FACTS devices optimal power flow (OPF) orthogonal multi‐swarm greedy selection sine cosine algorithm (OMGSCA) sine cosine algorithm (SCA) wind integration |
| url | https://doi.org/10.1002/eng2.70167 |
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