Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique
Abstract In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82193-1 |
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| author | Sarra Adiche Mhamed Larbi Djilali Toumi Riyadh Bouddou Mohit Bajaj Nasreddine Bouchikhi Abdallah Belabbes Ievgen Zaitsev |
| author_facet | Sarra Adiche Mhamed Larbi Djilali Toumi Riyadh Bouddou Mohit Bajaj Nasreddine Bouchikhi Abdallah Belabbes Ievgen Zaitsev |
| author_sort | Sarra Adiche |
| collection | DOAJ |
| description | Abstract In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG’s and improve the robustness of the system under varying load and generation conditions. Simulation findings under different tests have shown significant improvements in performance and computational simulation. The rise time is reduced by 60%, the overshoot is reduced by 80%, the THD of the voltage is reduced by 75% (from 0.99 to 0.20%), and the THD of the current is reduced by 69% (from 10.73 to 3.36%) compared to the conventional PI controller technique. Furthermore, voltage and current THD values were maintained below the IEEE-519 standard limits of 5% and 8%, respectively, for the power quality enhancement. Fluctuations in voltage and frequency were also maintained at 2% tolerance and 1% tolerance, respectively, across all voltage limits, which is consistent with international norms. Power-sharing errors were reduced by 50% after conducting the robustness tests against the DC supply and load disturbances. In addition, the proposed strategy outperforms the previous control techniques presented at the state of the art in terms of adaptability, stability and, especially, the ability to reduce the THD, which validates its effectiveness for MG systems control and optimization under uncertain conditions. |
| format | Article |
| id | doaj-art-93c6fe12d0b34df9b07fcdf19a4817be |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-93c6fe12d0b34df9b07fcdf19a4817be2025-08-20T02:43:25ZengNature PortfolioScientific Reports2045-23222024-12-0114114410.1038/s41598-024-82193-1Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance techniqueSarra Adiche0Mhamed Larbi1Djilali Toumi2Riyadh Bouddou3Mohit Bajaj4Nasreddine Bouchikhi5Abdallah Belabbes6Ievgen Zaitsev7Department of Electrical Engineering, L2GEGI Laboratory, University of TiaretDepartment of Electrical Engineering, L2GEGI Laboratory, University of TiaretDepartment of Electrical Engineering, L2GEGI Laboratory, University of TiaretDepartment of Electrical Engineering, Institute of Technology, University Centre of NaamaDepartment of Electrical Engineering, Graphic Era (Deemed to Be University)Department of Electrical Engineering, Institute of Technology, University Centre of NaamaInstitute of maintenance and Industrial security, University of Oran 2 Mohammed Ben AhmedDepartment of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of UkraineAbstract In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG’s and improve the robustness of the system under varying load and generation conditions. Simulation findings under different tests have shown significant improvements in performance and computational simulation. The rise time is reduced by 60%, the overshoot is reduced by 80%, the THD of the voltage is reduced by 75% (from 0.99 to 0.20%), and the THD of the current is reduced by 69% (from 10.73 to 3.36%) compared to the conventional PI controller technique. Furthermore, voltage and current THD values were maintained below the IEEE-519 standard limits of 5% and 8%, respectively, for the power quality enhancement. Fluctuations in voltage and frequency were also maintained at 2% tolerance and 1% tolerance, respectively, across all voltage limits, which is consistent with international norms. Power-sharing errors were reduced by 50% after conducting the robustness tests against the DC supply and load disturbances. In addition, the proposed strategy outperforms the previous control techniques presented at the state of the art in terms of adaptability, stability and, especially, the ability to reduce the THD, which validates its effectiveness for MG systems control and optimization under uncertain conditions.https://doi.org/10.1038/s41598-024-82193-1AC microgridsAdaptive PI controllerArtificial neural networkDroop controlVirtual impedance techniqueTotal harmonic distortion reduction |
| spellingShingle | Sarra Adiche Mhamed Larbi Djilali Toumi Riyadh Bouddou Mohit Bajaj Nasreddine Bouchikhi Abdallah Belabbes Ievgen Zaitsev Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique Scientific Reports AC microgrids Adaptive PI controller Artificial neural network Droop control Virtual impedance technique Total harmonic distortion reduction |
| title | Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique |
| title_full | Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique |
| title_fullStr | Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique |
| title_full_unstemmed | Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique |
| title_short | Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique |
| title_sort | advanced control strategy for ac microgrids a hybrid ann based adaptive pi controller with droop control and virtual impedance technique |
| topic | AC microgrids Adaptive PI controller Artificial neural network Droop control Virtual impedance technique Total harmonic distortion reduction |
| url | https://doi.org/10.1038/s41598-024-82193-1 |
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