From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter
This paper proposes an end-to-end (<inline-formula> <tex-math notation="LaTeX">$E2E$ </tex-math></inline-formula>) learning-based control policy to directly control a transformerless grid-tied three-level neutral-point-clamped (<italic>3L-NPC</italic>) i...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9771222/ |
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| author | Sherif A. Zaid Ihab S. Mohamed Abualkasim Bakeer Lantao Liu Hani Albalawi Mohamed E. Tawfiq Ahmed M. Kassem |
| author_facet | Sherif A. Zaid Ihab S. Mohamed Abualkasim Bakeer Lantao Liu Hani Albalawi Mohamed E. Tawfiq Ahmed M. Kassem |
| author_sort | Sherif A. Zaid |
| collection | DOAJ |
| description | This paper proposes an end-to-end (<inline-formula> <tex-math notation="LaTeX">$E2E$ </tex-math></inline-formula>) learning-based control policy to directly control a transformerless grid-tied three-level neutral-point-clamped (<italic>3L-NPC</italic>) inverter powered by a photovoltaic (<italic>PV</italic>) array. This <inline-formula> <tex-math notation="LaTeX">$E2E$ </tex-math></inline-formula> control policy is represented by an artificial neural network (<italic>ANN</italic>) and a time-delay neural network (<italic>TDNN</italic>), namely, <italic>ANN</italic> - and <italic>TDNN</italic> -based control policies, to properly estimate the <italic>optimal</italic> switching vector of the <italic>3L-NPC</italic>. With such learning-based control policy, there exists no need for deriving or understanding deeply the complex mathematical model of the <italic>3L-NPC</italic>, as the dynamics of both the system and the control scheme, as well as the cost function to be minimized, are learned via an end-to-end learning fashion that maps directly from the raw observations to the <italic>optimal</italic> switching states. This definitely eliminates the major barriers of the model-based control strategies (i.e., model predictive control (<italic>MPC</italic>)) such as (i) the need for an accurate system model, and (ii) the exponential increase in computational complexity. In order to train the two control policies, the conventional <italic>MPC</italic> is employed, as an expert, for acquiring a set of training data (i.e., input-output pairs) and, thereafter, for assessing our proposed control schemes. The proposed <italic>E2E</italic> control strategies are validated using MATLAB/Simulink software, where the impact of having different input features and training data are studied. With the proposed control policies, especially the <italic>TDNN</italic> -based control policy that has only one time-delay window, a high-quality sinusoidal grid current is achieved with low total harmonic distortion (<italic>THD</italic>), resulting in enhancing the power quality of the utility grid. In addition, the leakage current is minimized compared to the conventional <italic>MPC</italic> by more than 25%. However, the same dynamic behavior is almost obtained during the irradiation changes compared to the <italic>MPC</italic> strategy. Moreover, the experimental verification of the proposed <italic>E2E</italic> control strategy is implemented on the basis of the Hardware-in-the-Loop (HIL) simulator using the C2000TM-microcontroller-LaunchPadXL TMS320F28379D kit, demonstrating the applicability and good performance of our proposed control strategy under realistic conditions. |
| first_indexed | 2024-04-13T21:56:32Z |
| format | Article |
| id | doaj.art-7b8fdddb4ac04db7aa8b3d08b72b074e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T21:56:32Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7b8fdddb4ac04db7aa8b3d08b72b074e2022-12-22T02:28:15ZengIEEEIEEE Access2169-35362022-01-0110573095732610.1109/ACCESS.2022.31737529771222From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless InverterSherif A. Zaid0https://orcid.org/0000-0003-3099-9807Ihab S. Mohamed1https://orcid.org/0000-0003-3344-1614Abualkasim Bakeer2https://orcid.org/0000-0002-9418-5450Lantao Liu3https://orcid.org/0000-0002-6796-6817Hani Albalawi4https://orcid.org/0000-0001-6251-5008Mohamed E. Tawfiq5Ahmed M. Kassem6Electrical Engineering Department, Faculty of Engineering, University of Tabuk, Tabuk, Saudi ArabiaLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USADepartment of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, EgyptLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USAElectrical Engineering Department, Faculty of Engineering, University of Tabuk, Tabuk, Saudi ArabiaElectrical Engineering Department, Faculty of Engineering, University of Tabuk, Tabuk, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, EgyptThis paper proposes an end-to-end (<inline-formula> <tex-math notation="LaTeX">$E2E$ </tex-math></inline-formula>) learning-based control policy to directly control a transformerless grid-tied three-level neutral-point-clamped (<italic>3L-NPC</italic>) inverter powered by a photovoltaic (<italic>PV</italic>) array. This <inline-formula> <tex-math notation="LaTeX">$E2E$ </tex-math></inline-formula> control policy is represented by an artificial neural network (<italic>ANN</italic>) and a time-delay neural network (<italic>TDNN</italic>), namely, <italic>ANN</italic> - and <italic>TDNN</italic> -based control policies, to properly estimate the <italic>optimal</italic> switching vector of the <italic>3L-NPC</italic>. With such learning-based control policy, there exists no need for deriving or understanding deeply the complex mathematical model of the <italic>3L-NPC</italic>, as the dynamics of both the system and the control scheme, as well as the cost function to be minimized, are learned via an end-to-end learning fashion that maps directly from the raw observations to the <italic>optimal</italic> switching states. This definitely eliminates the major barriers of the model-based control strategies (i.e., model predictive control (<italic>MPC</italic>)) such as (i) the need for an accurate system model, and (ii) the exponential increase in computational complexity. In order to train the two control policies, the conventional <italic>MPC</italic> is employed, as an expert, for acquiring a set of training data (i.e., input-output pairs) and, thereafter, for assessing our proposed control schemes. The proposed <italic>E2E</italic> control strategies are validated using MATLAB/Simulink software, where the impact of having different input features and training data are studied. With the proposed control policies, especially the <italic>TDNN</italic> -based control policy that has only one time-delay window, a high-quality sinusoidal grid current is achieved with low total harmonic distortion (<italic>THD</italic>), resulting in enhancing the power quality of the utility grid. In addition, the leakage current is minimized compared to the conventional <italic>MPC</italic> by more than 25%. However, the same dynamic behavior is almost obtained during the irradiation changes compared to the <italic>MPC</italic> strategy. Moreover, the experimental verification of the proposed <italic>E2E</italic> control strategy is implemented on the basis of the Hardware-in-the-Loop (HIL) simulator using the C2000TM-microcontroller-LaunchPadXL TMS320F28379D kit, demonstrating the applicability and good performance of our proposed control strategy under realistic conditions.https://ieeexplore.ieee.org/document/9771222/End-to-end (E2E) control policyartificial neural network (ANN)time-delay neural network (TDNN)neutral-point-clamped (NPC) invertermodel predictive control (MPC)photovoltaic (PV) applications |
| spellingShingle | Sherif A. Zaid Ihab S. Mohamed Abualkasim Bakeer Lantao Liu Hani Albalawi Mohamed E. Tawfiq Ahmed M. Kassem From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter IEEE Access End-to-end (E2E) control policy artificial neural network (ANN) time-delay neural network (TDNN) neutral-point-clamped (NPC) inverter model predictive control (MPC) photovoltaic (PV) applications |
| title | From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter |
| title_full | From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter |
| title_fullStr | From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter |
| title_full_unstemmed | From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter |
| title_short | From <italic>MPC</italic>-Based to End-to-End <italic>(E2E)</italic> Learning-Based Control Policy for Grid-Tied <italic>3L-NPC</italic> Transformerless Inverter |
| title_sort | from italic mpc italic based to end to end italic e2e italic learning based control policy for grid tied italic 3l npc italic transformerless inverter |
| topic | End-to-end (E2E) control policy artificial neural network (ANN) time-delay neural network (TDNN) neutral-point-clamped (NPC) inverter model predictive control (MPC) photovoltaic (PV) applications |
| url | https://ieeexplore.ieee.org/document/9771222/ |
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