Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking
This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP,...
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| Format: | Article |
| Language: | English |
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University of Baghdad
2023-10-01
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| Series: | Journal of Engineering |
| Subjects: | |
| Online Access: | https://joe.uobaghdad.edu.iq/index.php/main/article/view/1937 |
| _version_ | 1797635628365512704 |
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| author | Enas Hamid Ibrahem Nadia Qasim Mohammed Hanan Mikhael D. Habbi |
| author_facet | Enas Hamid Ibrahem Nadia Qasim Mohammed Hanan Mikhael D. Habbi |
| author_sort | Enas Hamid Ibrahem |
| collection | DOAJ |
| description |
This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.
Moreover, the proposed controller is tested under static and dynamic load conditions. The simulation and models are done by using MATLAB/Simulink. The simulation results from the proposed NARMA-L2 controller have been compared with existing Perturb and observe PO-MPPT and Incremental Conductance INC -MPPT methods.
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| first_indexed | 2024-03-11T12:23:07Z |
| format | Article |
| id | doaj.art-d208acdf5d9340d0a2e0e1c3c81f75de |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-11T12:23:07Z |
| publishDate | 2023-10-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-d208acdf5d9340d0a2e0e1c3c81f75de2023-11-06T19:20:26ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392023-10-01291010.31026/j.eng.2023.10.02Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point TrackingEnas Hamid Ibrahem0Nadia Qasim Mohammed1Hanan Mikhael D. Habbi2University of Baghdad- Collage of EngineeringUnivesrity of Baghdad/ College of Engineering/ Electrical DepartmentElectrical and Computer Engineering, Oakland University, Michigan, USA This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time. Moreover, the proposed controller is tested under static and dynamic load conditions. The simulation and models are done by using MATLAB/Simulink. The simulation results from the proposed NARMA-L2 controller have been compared with existing Perturb and observe PO-MPPT and Incremental Conductance INC -MPPT methods. https://joe.uobaghdad.edu.iq/index.php/main/article/view/1937MicrogridSolar PVHERMaximum power point trackingDeep learningPO-MPPT |
| spellingShingle | Enas Hamid Ibrahem Nadia Qasim Mohammed Hanan Mikhael D. Habbi Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking Journal of Engineering Microgrid Solar PV HER Maximum power point tracking Deep learning PO-MPPT |
| title | Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking |
| title_full | Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking |
| title_fullStr | Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking |
| title_full_unstemmed | Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking |
| title_short | Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking |
| title_sort | microgrid integration based on deep learning narma l2 controller for maximum power point tracking |
| topic | Microgrid Solar PV HER Maximum power point tracking Deep learning PO-MPPT |
| url | https://joe.uobaghdad.edu.iq/index.php/main/article/view/1937 |
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