Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer
As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (PVEs) for testing and comparing control strategies, such as Maximum Power Point Tracking (MPPT), is growing. PVEs allow for consistent testing by accurately simulating the behavior of PV pane...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2024-11-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.iust.ac.ir/article-1-3402-en.pdf |
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| _version_ | 1841550993052925952 |
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| author | Aboubakeur HADJAISSA Mohammed BENMILOUD khaled AMEUR HALIMA BOUCHENAK Maria DIMEH |
| author_facet | Aboubakeur HADJAISSA Mohammed BENMILOUD khaled AMEUR HALIMA BOUCHENAK Maria DIMEH |
| author_sort | Aboubakeur HADJAISSA |
| collection | DOAJ |
| description | As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (PVEs) for testing and comparing control strategies, such as Maximum Power Point Tracking (MPPT), is growing. PVEs allow for consistent testing by accurately simulating the behavior of PV panels, free from external influences like irradiance and temperature variations. This study focuses on developing a PVE model using deep learning techniques, specifically a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) with backpropagation as the learning algorithm. The ANN is integrated with a DC-DC push-pull converter controlled via a Linear Quadratic Regulator (LQR) strategy. The ANN emulates the nonlinear characteristics of PV panels, generating precise reference currents. Additionally, the use of a single voltage sensor paired with a current observer enhances control signal accuracy and reduces the PVE system's hardware requirements. Comparative analysis demonstrates that the proposed LQR-based controller significantly outperforms conventional PID controllers in both steady-state error and response time. |
| format | Article |
| id | doaj-art-6aedf730a6f443a7ba2d47e282495301 |
| institution | Kabale University |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-6aedf730a6f443a7ba2d47e2824953012025-01-09T18:47:15ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902024-11-012046878Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current ObserverAboubakeur HADJAISSA0Mohammed BENMILOUD1khaled AMEUR2HALIMA BOUCHENAK3Maria DIMEH4 LACoSERE Laboratory, University of Laghouat, Algeria LACoSERE Laboratory, University of Laghouat, Algeria LACoSERE Laboratory, University of Laghouat, Algeria University of Laghouat University of Laghouat As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (PVEs) for testing and comparing control strategies, such as Maximum Power Point Tracking (MPPT), is growing. PVEs allow for consistent testing by accurately simulating the behavior of PV panels, free from external influences like irradiance and temperature variations. This study focuses on developing a PVE model using deep learning techniques, specifically a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) with backpropagation as the learning algorithm. The ANN is integrated with a DC-DC push-pull converter controlled via a Linear Quadratic Regulator (LQR) strategy. The ANN emulates the nonlinear characteristics of PV panels, generating precise reference currents. Additionally, the use of a single voltage sensor paired with a current observer enhances control signal accuracy and reduces the PVE system's hardware requirements. Comparative analysis demonstrates that the proposed LQR-based controller significantly outperforms conventional PID controllers in both steady-state error and response time.http://ijeee.iust.ac.ir/article-1-3402-en.pdfphotovoltaic emulators (pves)artificial neural network (ann)dc-dc push-pull converterlqr strategyluenberger observer. |
| spellingShingle | Aboubakeur HADJAISSA Mohammed BENMILOUD khaled AMEUR HALIMA BOUCHENAK Maria DIMEH Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer Iranian Journal of Electrical and Electronic Engineering photovoltaic emulators (pves) artificial neural network (ann) dc-dc push-pull converter lqr strategy luenberger observer. |
| title | Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer |
| title_full | Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer |
| title_fullStr | Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer |
| title_full_unstemmed | Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer |
| title_short | Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer |
| title_sort | advanced photovoltaic emulator with ann based modeling using a dc dc push pull converter and lqr control with current observer |
| topic | photovoltaic emulators (pves) artificial neural network (ann) dc-dc push-pull converter lqr strategy luenberger observer. |
| url | http://ijeee.iust.ac.ir/article-1-3402-en.pdf |
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