Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks
The voltage source inverters in microgrids often rely on the droop control method integrated with voltage and inner current control loops in order to provide a reliable electric power supply. This research aims to present a Cascade-Forward Neural Network (CFNN) droop control method that manages inve...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Circuits and Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9962156/ |
| _version_ | 1798015621796986880 |
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| author | Mohamad Alzayed Michel Lemaire Sina Zarrabian Hicham Chaoui Daniel Massicotte |
| author_facet | Mohamad Alzayed Michel Lemaire Sina Zarrabian Hicham Chaoui Daniel Massicotte |
| author_sort | Mohamad Alzayed |
| collection | DOAJ |
| description | The voltage source inverters in microgrids often rely on the droop control method integrated with voltage and inner current control loops in order to provide a reliable electric power supply. This research aims to present a Cascade-Forward Neural Network (CFNN) droop control method that manages inverter-based microgrids under grid-connected/islanded operating modes. The proposed method operates the inverter in a bi-directional technique for a wide range of battery energy storage systems or any other distributed generation systems. The proposed strategy uses the CFNN to learn the inverter’s nonlinear model to achieve accurate demand and reference power tracking under different operating conditions for smart grid applications. Additionally, it reformulates the grid control concept to drive the inverter based on the optimal conditions by considering the power demand, reference power, equipment size, and disturbances. Also, it does not require any tuning procedure. The power tracking and operating performance of the proposed CFNN controller are evaluated through several experimental tests using the power hardware-in-the-loop (PHIL) methodology in different scenarios. All results are matched with the proven conventional strategy to confirm its effectiveness. |
| first_indexed | 2024-04-11T15:37:45Z |
| format | Article |
| id | doaj.art-0f5d7015e4064ae6b09103c8fb615d38 |
| institution | Directory Open Access Journal |
| issn | 2644-1225 |
| language | English |
| last_indexed | 2024-04-11T15:37:45Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Circuits and Systems |
| spelling | doaj.art-0f5d7015e4064ae6b09103c8fb615d382022-12-22T04:15:55ZengIEEEIEEE Open Journal of Circuits and Systems2644-12252022-01-01329830810.1109/OJCAS.2022.32061209962156Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural NetworksMohamad Alzayed0https://orcid.org/0000-0003-4190-1828Michel Lemaire1Sina Zarrabian2Hicham Chaoui3https://orcid.org/0000-0001-8728-3653Daniel Massicotte4https://orcid.org/0000-0002-7807-7919Department of Electrical and Computer Engineering, Laboratory of Signal and System Integration, Université du Québec à Trois-Rivières, Trois-Rivières, CanadaDepartment of Electrical and Computer Engineering, Laboratory of Signal and System Integration, Université du Québec à Trois-Rivières, Trois-Rivières, CanadaDepartment of Electrical Engineering, State University of New York, Maritime College, Throggs Neck, NY, USADepartment of Electronics, Intelligent Robotic and Energy Systems Research Group, Carleton University, Ottawa, CanadaDepartment of Electrical and Computer Engineering, Laboratory of Signal and System Integration, Université du Québec à Trois-Rivières, Trois-Rivières, CanadaThe voltage source inverters in microgrids often rely on the droop control method integrated with voltage and inner current control loops in order to provide a reliable electric power supply. This research aims to present a Cascade-Forward Neural Network (CFNN) droop control method that manages inverter-based microgrids under grid-connected/islanded operating modes. The proposed method operates the inverter in a bi-directional technique for a wide range of battery energy storage systems or any other distributed generation systems. The proposed strategy uses the CFNN to learn the inverter’s nonlinear model to achieve accurate demand and reference power tracking under different operating conditions for smart grid applications. Additionally, it reformulates the grid control concept to drive the inverter based on the optimal conditions by considering the power demand, reference power, equipment size, and disturbances. Also, it does not require any tuning procedure. The power tracking and operating performance of the proposed CFNN controller are evaluated through several experimental tests using the power hardware-in-the-loop (PHIL) methodology in different scenarios. All results are matched with the proven conventional strategy to confirm its effectiveness.https://ieeexplore.ieee.org/document/9962156/Distributed generationdroop controlinverter-based power systemmicrogridcascadeforward neural network |
| spellingShingle | Mohamad Alzayed Michel Lemaire Sina Zarrabian Hicham Chaoui Daniel Massicotte Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks IEEE Open Journal of Circuits and Systems Distributed generation droop control inverter-based power system microgrid cascadeforward neural network |
| title | Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks |
| title_full | Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks |
| title_fullStr | Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks |
| title_full_unstemmed | Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks |
| title_short | Droop-Controlled Bidirectional Inverter-Based Microgrid Using Cascade-Forward Neural Networks |
| title_sort | droop controlled bidirectional inverter based microgrid using cascade forward neural networks |
| topic | Distributed generation droop control inverter-based power system microgrid cascadeforward neural network |
| url | https://ieeexplore.ieee.org/document/9962156/ |
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