Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)                   IJEEE 2024, 20(4): 3402-3402 | Back to browse issues page

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HADJAISSA A, BENMILOUD M, AMEUR K, BOUCHENAK H, DIMEH M. Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer. IJEEE 2024; 20 (4) :3402-3402
URL: http://ijeee.iust.ac.ir/article-1-3402-en.html
Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer
Aboubakeur HADJAISSA , Mohammed BENMILOUD , Khaled AMEUR , HALIMA BOUCHENAK , Maria DIMEH
Abstract:   (519 Views)
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.
Keywords: Photovoltaic Emulators (PVEs), Artificial Neural Network (ANN), DC-DC Push-Pull Converter, LQR Strategy, Luenberger observer.
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Type of Study: Closed - 2024 Special Issue on Applications of Deep Learning in Electrical and Electronic Engineerin | Subject: Neural Networks
Received: 2024/08/07 | Revised: 2024/12/05 | Accepted: 2024/10/22
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.