Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature
This paper presents an artificial neural network-based maximum power point tracking (MPPT) method. Where dual ANNs predict solar irradiance and temperature. Next, an adaptive computation block determines the maximum power point (MPP). The proposed MPPT method stabilizes output power at the MPP, unli...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003603 |
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| author | Ahmad M.A. Malkawi Zuhour A.B. Alsaqqa Tareq O. Al-Mosa Wa'el M. JadAllah Mohannad M.H. Sadeddin Ayman Al-Quraan Mohammad AlMashagbeh |
| author_facet | Ahmad M.A. Malkawi Zuhour A.B. Alsaqqa Tareq O. Al-Mosa Wa'el M. JadAllah Mohannad M.H. Sadeddin Ayman Al-Quraan Mohammad AlMashagbeh |
| author_sort | Ahmad M.A. Malkawi |
| collection | DOAJ |
| description | This paper presents an artificial neural network-based maximum power point tracking (MPPT) method. Where dual ANNs predict solar irradiance and temperature. Next, an adaptive computation block determines the maximum power point (MPP). The proposed MPPT method stabilizes output power at the MPP, unlike traditional MPPT methods, which oscillate. The suggested tracker does not require expensive solar irradiance and multiple temperature sensors, unlike trackers that do. Additionally, the MPPT can handle rapid solar irradiance changes. The ANNs integrated with the adaptive block were evaluated to validate the system's capacity to estimate solar irradiance, temperature, and maximum power point. The solar system was integrated into a Microgrid and subsequently simulated using MATLAB/Simulink to evaluate the robustness of the suggested approach under steady-state and during sudden changes in solar irradiance and temperature. The proposed solar system achieves a steady state within 30 ms following a sudden solar irradiance or temperature change. The method achieves pest estimate at 1000 W/m2 and 45 °C with an efficiency of 99.9 %. Additionally, at 1000 W/m2 and 25 °C, the system achieves 99.8 % efficiency. Under the worst-case scenario, the proposed system demonstrates a relative error of approximately 6.1 % when estimating solar irradiance at 600 W/m2 and 45 °C and a relative error of around 21.1 % when estimating temperature at 700 W/m2 and 25 °C. Nevertheless, the efficiency under the worst-case scenario is 94.2 % when the power density is 500 W/m2 and the temperature is 45 °C. |
| format | Article |
| id | doaj-art-aa9cb66fcbf54615b90972a96acc8f46 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-aa9cb66fcbf54615b90972a96acc8f462025-02-09T05:01:06ZengElsevierResults in Engineering2590-12302025-03-0125104275Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperatureAhmad M.A. Malkawi0Zuhour A.B. Alsaqqa1Tareq O. Al-Mosa2Wa'el M. JadAllah3Mohannad M.H. Sadeddin4Ayman Al-Quraan5Mohammad AlMashagbeh6Mechatronics Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan; Corresponding author.Mechatronics Engineering Department, School of Engineering, The University of Jordan, Amman 11942, JordanMechatronics Engineering Department, School of Engineering, The University of Jordan, Amman 11942, JordanMechatronics Engineering Department, School of Engineering, The University of Jordan, Amman 11942, JordanMechatronics Engineering Department, School of Engineering, The University of Jordan, Amman 11942, JordanElectrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, 21163, JordanMechatronics Engineering Department, School of Engineering, The University of Jordan, Amman 11942, JordanThis paper presents an artificial neural network-based maximum power point tracking (MPPT) method. Where dual ANNs predict solar irradiance and temperature. Next, an adaptive computation block determines the maximum power point (MPP). The proposed MPPT method stabilizes output power at the MPP, unlike traditional MPPT methods, which oscillate. The suggested tracker does not require expensive solar irradiance and multiple temperature sensors, unlike trackers that do. Additionally, the MPPT can handle rapid solar irradiance changes. The ANNs integrated with the adaptive block were evaluated to validate the system's capacity to estimate solar irradiance, temperature, and maximum power point. The solar system was integrated into a Microgrid and subsequently simulated using MATLAB/Simulink to evaluate the robustness of the suggested approach under steady-state and during sudden changes in solar irradiance and temperature. The proposed solar system achieves a steady state within 30 ms following a sudden solar irradiance or temperature change. The method achieves pest estimate at 1000 W/m2 and 45 °C with an efficiency of 99.9 %. Additionally, at 1000 W/m2 and 25 °C, the system achieves 99.8 % efficiency. Under the worst-case scenario, the proposed system demonstrates a relative error of approximately 6.1 % when estimating solar irradiance at 600 W/m2 and 45 °C and a relative error of around 21.1 % when estimating temperature at 700 W/m2 and 25 °C. Nevertheless, the efficiency under the worst-case scenario is 94.2 % when the power density is 500 W/m2 and the temperature is 45 °C.http://www.sciencedirect.com/science/article/pii/S2590123025003603Maximum power point trackingPhotovoltaicArtificial neural networkMicrogridRenewable energy sources |
| spellingShingle | Ahmad M.A. Malkawi Zuhour A.B. Alsaqqa Tareq O. Al-Mosa Wa'el M. JadAllah Mohannad M.H. Sadeddin Ayman Al-Quraan Mohammad AlMashagbeh Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature Results in Engineering Maximum power point tracking Photovoltaic Artificial neural network Microgrid Renewable energy sources |
| title | Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature |
| title_full | Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature |
| title_fullStr | Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature |
| title_full_unstemmed | Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature |
| title_short | Maximum power point tracking enhancement for PV in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature |
| title_sort | maximum power point tracking enhancement for pv in microgrids systems using dual artificial neural networks to estimate solar irradiance and temperature |
| topic | Maximum power point tracking Photovoltaic Artificial neural network Microgrid Renewable energy sources |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025003603 |
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