Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization Algorithm
As the proportion of renewable energy sources integrated into the power grid increases, it imposes significant volatility on the grid, leading to uneven load distribution across certain transmission lines. Rotating Power Flow Controllers (RPFCs) based on Rotating Phase-Shifting Transformers (RPSTs)...
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MDPI AG
2025-02-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/30/1/20 |
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| author | Ziyang Zhang Jiaoxin Jia Waseem Aslam Abubakar Siddique Fahad R. Albogamy |
| author_facet | Ziyang Zhang Jiaoxin Jia Waseem Aslam Abubakar Siddique Fahad R. Albogamy |
| author_sort | Ziyang Zhang |
| collection | DOAJ |
| description | As the proportion of renewable energy sources integrated into the power grid increases, it imposes significant volatility on the grid, leading to uneven load distribution across certain transmission lines. Rotating Power Flow Controllers (RPFCs) based on Rotating Phase-Shifting Transformers (RPSTs) offer a viable solution to such issues in lines rated at 10 kV and below. This paper begins with a brief introduction to RPFCs, followed by the modeling of their topology for a single-circuit line and the derivation of active and reactive power flow formulas. Notably, this paper introduces intelligent optimization algorithms to this field for the first time, employing an improved hybrid particle swarm optimization (HPSO) algorithm to control the active power while keeping the reactive power constant and subsequently adjusting the reactive power while maintaining the active power steady, thereby achieving power regulation. Using Matlab/Simulink simulations, this strategy was compared with adaptive adjustment strategies, verifying that it exhibits reduced power fluctuations and overshoots during the adjustment process, thus confirming the effectiveness of the adjustment scheme. By leveraging this algorithm in conjunction with simulations, a Q-P operating range diagram for transmission lines was plotted, determining the adjustable range of actual power. |
| format | Article |
| id | doaj-art-53de29e2121c4ffdb500575c4eb0eff7 |
| institution | DOAJ |
| issn | 1300-686X 2297-8747 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Mathematical and Computational Applications |
| spelling | doaj-art-53de29e2121c4ffdb500575c4eb0eff72025-08-20T03:12:11ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472025-02-013012010.3390/mca30010020Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization AlgorithmZiyang Zhang0Jiaoxin Jia1Waseem Aslam2Abubakar Siddique3Fahad R. Albogamy4Department of Electric Engineering, North China Electric Power University, Baoding 071000, ChinaDepartment of Electric Engineering, North China Electric Power University, Baoding 071000, ChinaDepartment of Electrical, Electronics and Computer Systems, University of Sargodha (UOS) Sargodha, Punjab 40100, PakistanDepartment of Electrical & Biomedical Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan 64200, PakistanComputer Sciences Program, Department of Mathematics, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaAs the proportion of renewable energy sources integrated into the power grid increases, it imposes significant volatility on the grid, leading to uneven load distribution across certain transmission lines. Rotating Power Flow Controllers (RPFCs) based on Rotating Phase-Shifting Transformers (RPSTs) offer a viable solution to such issues in lines rated at 10 kV and below. This paper begins with a brief introduction to RPFCs, followed by the modeling of their topology for a single-circuit line and the derivation of active and reactive power flow formulas. Notably, this paper introduces intelligent optimization algorithms to this field for the first time, employing an improved hybrid particle swarm optimization (HPSO) algorithm to control the active power while keeping the reactive power constant and subsequently adjusting the reactive power while maintaining the active power steady, thereby achieving power regulation. Using Matlab/Simulink simulations, this strategy was compared with adaptive adjustment strategies, verifying that it exhibits reduced power fluctuations and overshoots during the adjustment process, thus confirming the effectiveness of the adjustment scheme. By leveraging this algorithm in conjunction with simulations, a Q-P operating range diagram for transmission lines was plotted, determining the adjustable range of actual power.https://www.mdpi.com/2297-8747/30/1/20rotary power flow controllerpower regulationimproved hybrid particle swarm optimization algorithm |
| spellingShingle | Ziyang Zhang Jiaoxin Jia Waseem Aslam Abubakar Siddique Fahad R. Albogamy Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization Algorithm Mathematical and Computational Applications rotary power flow controller power regulation improved hybrid particle swarm optimization algorithm |
| title | Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization Algorithm |
| title_full | Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization Algorithm |
| title_fullStr | Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization Algorithm |
| title_short | Control Strategy of a Rotating Power Flow Controller Based on an Improved Hybrid Particle Swarm Optimization Algorithm |
| title_sort | control strategy of a rotating power flow controller based on an improved hybrid particle swarm optimization algorithm |
| topic | rotary power flow controller power regulation improved hybrid particle swarm optimization algorithm |
| url | https://www.mdpi.com/2297-8747/30/1/20 |
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