Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters
This paper proposes a model predictive control (MPC)-based power quality optimization method designed to enhance the low-voltage ride-through (LVRT) capability of grid-connected inverters under various grid voltage sag conditions, while achieving multi-objective power quality optimization. Tradition...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004387 |
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| author | Haining Wang Jiang Geng Bowen Feng Di Xie Peng Zhang Liangliang Wang Jigang Yao |
| author_facet | Haining Wang Jiang Geng Bowen Feng Di Xie Peng Zhang Liangliang Wang Jigang Yao |
| author_sort | Haining Wang |
| collection | DOAJ |
| description | This paper proposes a model predictive control (MPC)-based power quality optimization method designed to enhance the low-voltage ride-through (LVRT) capability of grid-connected inverters under various grid voltage sag conditions, while achieving multi-objective power quality optimization. Traditional PI/PR current loop control structures, which rely on positive and negative sequence separation, suffer from issues such as filtering delays, complex parameter tuning, and performance degradation due to deteriorated power quality, adversely affecting the ride-through process. The proposed method eliminates the need for current positive and negative sequence separation and filtering voltage feedforward processing, effectively preventing overcurrent during deep voltage sags (0.1 per unit). It establishes a general formula for calculating the reference current for power/current quality optimization in the αβ axis. By directly adjusting the correction coefficient matrix, the method enables seamless switching and unified optimized control among power fluctuation, negative sequence current, and harmonic current. To quantitatively demonstrate the superiority of the proposed method, an evaluation framework based on the weighted rank sum ratio (WRSR) method is established. The results indicate a performance improvement of approximately 150 % compared to traditional methods within the optimal correction coefficient interval. The effectiveness of the proposed method under different grid fault sag conditions is validated through MATLAB simulations and the Starsim experimental platform. The results show that the proposed method exhibits excellent dynamic performance, with a settling time of about 10 ms, significantly enhancing the LVRT capability of grid-connected inverters and meeting grid code requirements. |
| format | Article |
| id | doaj-art-16b3428847334aaba261d86e90468f9d |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-16b3428847334aaba261d86e90468f9d2025-08-20T03:41:26ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011089010.1016/j.ijepes.2025.110890Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected invertersHaining Wang0Jiang Geng1Bowen Feng2Di Xie3Peng Zhang4Liangliang Wang5Jigang Yao6School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui 230009, ChinaSchool of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui 230009, China; Corresponding author at: Jiang Geng, MEng, School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui 230009, China.School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui 230009, ChinaGuangdong HYNN Technologies Co., Ltd., Dongguan, Guangdong 523878, ChinaSchool of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui 230009, ChinaGuangdong HYNN Technologies Co., Ltd., Dongguan, Guangdong 523878, ChinaGuangdong HYNN Technologies Co., Ltd., Dongguan, Guangdong 523878, ChinaThis paper proposes a model predictive control (MPC)-based power quality optimization method designed to enhance the low-voltage ride-through (LVRT) capability of grid-connected inverters under various grid voltage sag conditions, while achieving multi-objective power quality optimization. Traditional PI/PR current loop control structures, which rely on positive and negative sequence separation, suffer from issues such as filtering delays, complex parameter tuning, and performance degradation due to deteriorated power quality, adversely affecting the ride-through process. The proposed method eliminates the need for current positive and negative sequence separation and filtering voltage feedforward processing, effectively preventing overcurrent during deep voltage sags (0.1 per unit). It establishes a general formula for calculating the reference current for power/current quality optimization in the αβ axis. By directly adjusting the correction coefficient matrix, the method enables seamless switching and unified optimized control among power fluctuation, negative sequence current, and harmonic current. To quantitatively demonstrate the superiority of the proposed method, an evaluation framework based on the weighted rank sum ratio (WRSR) method is established. The results indicate a performance improvement of approximately 150 % compared to traditional methods within the optimal correction coefficient interval. The effectiveness of the proposed method under different grid fault sag conditions is validated through MATLAB simulations and the Starsim experimental platform. The results show that the proposed method exhibits excellent dynamic performance, with a settling time of about 10 ms, significantly enhancing the LVRT capability of grid-connected inverters and meeting grid code requirements.http://www.sciencedirect.com/science/article/pii/S0142061525004387Model predictive controlPower qualityVoltage sagsLow-voltage ride-throughCapability evaluation |
| spellingShingle | Haining Wang Jiang Geng Bowen Feng Di Xie Peng Zhang Liangliang Wang Jigang Yao Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters International Journal of Electrical Power & Energy Systems Model predictive control Power quality Voltage sags Low-voltage ride-through Capability evaluation |
| title | Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters |
| title_full | Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters |
| title_fullStr | Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters |
| title_full_unstemmed | Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters |
| title_short | Application and impact of multi-power quality objective optimization control in low-voltage ride-through for grid-connected inverters |
| title_sort | application and impact of multi power quality objective optimization control in low voltage ride through for grid connected inverters |
| topic | Model predictive control Power quality Voltage sags Low-voltage ride-through Capability evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525004387 |
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