An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm
Maximizing the output power of photovoltaic (PV) systems is crucial in all PV applications to improve energy efficiency and system performance. Maximum power point tracking (MPPT) is utilized in PV systems to track the voltage that maximizes their output power under varying conditions. In this paper...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10977946/ |
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| author | Mohammad Rashwan Karim Diab Ahmed Elsergany Mamoun F. Abdel-Hafez Ala A. Hussein |
| author_facet | Mohammad Rashwan Karim Diab Ahmed Elsergany Mamoun F. Abdel-Hafez Ala A. Hussein |
| author_sort | Mohammad Rashwan |
| collection | DOAJ |
| description | Maximizing the output power of photovoltaic (PV) systems is crucial in all PV applications to improve energy efficiency and system performance. Maximum power point tracking (MPPT) is utilized in PV systems to track the voltage that maximizes their output power under varying conditions. In this paper, an enhanced MPPT estimation algorithm is proposed based on the H-adaptive extended Kalman filter (EKF). The proposed method adapts to possible changes in the system’s noise statistics due to variations in irradiance, operating temperature, and system’s aging. The approach is compared to common MPPT estimation techniques; namely the Perturb and Observe (P&O) method and the EKF. The method was validated using experimental data collected from a PV array, to demonstrate practical applicability. Results showcase the adaptability of the proposed approach to variations in the process noise covariance, measurement noise covariance, and the dynamic system scaling factor parameter. These attributes are essential for a sustained extraction of the maximum power from the PV system. |
| format | Article |
| id | doaj-art-76f6d50ad35f4352a38b60aec59b62d7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-76f6d50ad35f4352a38b60aec59b62d72025-08-20T03:11:26ZengIEEEIEEE Access2169-35362025-01-0113765167652710.1109/ACCESS.2025.356465710977946An Adaptive, Model-Robust, PV Maximum Power Point Tracking AlgorithmMohammad Rashwan0https://orcid.org/0009-0000-6025-6727Karim Diab1https://orcid.org/0009-0000-1890-3230Ahmed Elsergany2https://orcid.org/0000-0002-1117-2962Mamoun F. Abdel-Hafez3https://orcid.org/0000-0002-9010-4094Ala A. Hussein4https://orcid.org/0000-0002-8867-3132Mechatronics Graduate Program, American University of Sharjah, Sharjah, United Arab EmiratesMechatronics Graduate Program, American University of Sharjah, Sharjah, United Arab EmiratesMechanical Engineering Department, American University of Sharjah, Sharjah, United Arab EmiratesMechanical Engineering Department, American University of Sharjah, Sharjah, United Arab EmiratesElectrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar, Saudi ArabiaMaximizing the output power of photovoltaic (PV) systems is crucial in all PV applications to improve energy efficiency and system performance. Maximum power point tracking (MPPT) is utilized in PV systems to track the voltage that maximizes their output power under varying conditions. In this paper, an enhanced MPPT estimation algorithm is proposed based on the H-adaptive extended Kalman filter (EKF). The proposed method adapts to possible changes in the system’s noise statistics due to variations in irradiance, operating temperature, and system’s aging. The approach is compared to common MPPT estimation techniques; namely the Perturb and Observe (P&O) method and the EKF. The method was validated using experimental data collected from a PV array, to demonstrate practical applicability. Results showcase the adaptability of the proposed approach to variations in the process noise covariance, measurement noise covariance, and the dynamic system scaling factor parameter. These attributes are essential for a sustained extraction of the maximum power from the PV system.https://ieeexplore.ieee.org/document/10977946/Adaptive choice of process noise covarianceextended Kalman filterH-adaptive filtermaximum power point trackingphotovoltaic |
| spellingShingle | Mohammad Rashwan Karim Diab Ahmed Elsergany Mamoun F. Abdel-Hafez Ala A. Hussein An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm IEEE Access Adaptive choice of process noise covariance extended Kalman filter H-adaptive filter maximum power point tracking photovoltaic |
| title | An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm |
| title_full | An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm |
| title_fullStr | An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm |
| title_full_unstemmed | An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm |
| title_short | An Adaptive, Model-Robust, PV Maximum Power Point Tracking Algorithm |
| title_sort | adaptive model robust pv maximum power point tracking algorithm |
| topic | Adaptive choice of process noise covariance extended Kalman filter H-adaptive filter maximum power point tracking photovoltaic |
| url | https://ieeexplore.ieee.org/document/10977946/ |
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