Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation
Direct power control (DPC) is among the most popular control schemes used in renewable energy because of its many advantages such as simplicity, ease of execution, and speed of response compared to other controls. However, this method is characterized by defects and problems that limit its use, such...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/24/4106 |
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| author | Hamid Chojaa Aziz Derouich Seif Eddine Chehaidia Othmane Zamzoum Mohammed Taoussi Habib Benbouhenni Said Mahfoud |
| author_facet | Hamid Chojaa Aziz Derouich Seif Eddine Chehaidia Othmane Zamzoum Mohammed Taoussi Habib Benbouhenni Said Mahfoud |
| author_sort | Hamid Chojaa |
| collection | DOAJ |
| description | Direct power control (DPC) is among the most popular control schemes used in renewable energy because of its many advantages such as simplicity, ease of execution, and speed of response compared to other controls. However, this method is characterized by defects and problems that limit its use, such as a large number of ripples at the levels of torque and active power, and a decrease in the quality of the power as a result of using the hysteresis controller to regulate the capacities. In this paper, a new idea of DPC using artificial neural networks (ANNs) is proposed to overcome these problems and defects, in which the proposed DPC of the doubly fed induction generators (DFIGs) is experimentally verified. ANN algorithms were used to compensate the hysteresis controller and switching table, whereby the results obtained from the proposed intelligent DPC technique are compared with both the classical DPC strategy and backstepping control. A comparison is made between the three proposed controls in terms of ripple ratio, durability, response time, current quality, and reference tracking, using several different tests. The experimental and simulation results extracted from dSPACE DS1104 Controller card Real-Time Interface (RTI) and Matlab/Simulink environment, respectively, have proven the robustness and the effectiveness of the designed intelligence DPC of the DFIG compared to traditional and backstepping controls in terms of the harmonic distortion of the stator current, dynamic response, precision, reference tracking ability, power ripples, robustness, overshoot, and stability. |
| first_indexed | 2024-03-09T17:00:56Z |
| format | Article |
| id | doaj.art-c5a82e1d204f4f43ade0ffc63f3300da |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:00:56Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c5a82e1d204f4f43ade0ffc63f3300da2023-11-24T14:30:20ZengMDPI AGElectronics2079-92922022-12-011124410610.3390/electronics11244106Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental ValidationHamid Chojaa0Aziz Derouich1Seif Eddine Chehaidia2Othmane Zamzoum3Mohammed Taoussi4Habib Benbouhenni5Said Mahfoud6Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoIndustrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoMechanical Engineering Department, National Polytechnic School of Constantine (ENPC), Ali Mendjeli University City, BP 75A RP, Ali Mendjeli, Constantine 25000, AlgeriaIndustrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoIndustrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoDepartment of Electrical & Electronics Engineering, Faculty of Engineering and Architecture, Nisantasi University, 34398 Istanbul, TurkeyIndustrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoDirect power control (DPC) is among the most popular control schemes used in renewable energy because of its many advantages such as simplicity, ease of execution, and speed of response compared to other controls. However, this method is characterized by defects and problems that limit its use, such as a large number of ripples at the levels of torque and active power, and a decrease in the quality of the power as a result of using the hysteresis controller to regulate the capacities. In this paper, a new idea of DPC using artificial neural networks (ANNs) is proposed to overcome these problems and defects, in which the proposed DPC of the doubly fed induction generators (DFIGs) is experimentally verified. ANN algorithms were used to compensate the hysteresis controller and switching table, whereby the results obtained from the proposed intelligent DPC technique are compared with both the classical DPC strategy and backstepping control. A comparison is made between the three proposed controls in terms of ripple ratio, durability, response time, current quality, and reference tracking, using several different tests. The experimental and simulation results extracted from dSPACE DS1104 Controller card Real-Time Interface (RTI) and Matlab/Simulink environment, respectively, have proven the robustness and the effectiveness of the designed intelligence DPC of the DFIG compared to traditional and backstepping controls in terms of the harmonic distortion of the stator current, dynamic response, precision, reference tracking ability, power ripples, robustness, overshoot, and stability.https://www.mdpi.com/2079-9292/11/24/4106artificial neural networkdirect power controlbackstepping controldoubly-fed induction generatordSPACE DS1104 controller |
| spellingShingle | Hamid Chojaa Aziz Derouich Seif Eddine Chehaidia Othmane Zamzoum Mohammed Taoussi Habib Benbouhenni Said Mahfoud Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation Electronics artificial neural network direct power control backstepping control doubly-fed induction generator dSPACE DS1104 controller |
| title | Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation |
| title_full | Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation |
| title_fullStr | Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation |
| title_full_unstemmed | Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation |
| title_short | Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation |
| title_sort | enhancement of direct power control by using artificial neural network for a doubly fed induction generator based wecs an experimental validation |
| topic | artificial neural network direct power control backstepping control doubly-fed induction generator dSPACE DS1104 controller |
| url | https://www.mdpi.com/2079-9292/11/24/4106 |
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