Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications
Abstract Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their...
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| Format: | Article |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-58852-8 |
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| author | K. Krishnaram T. Suresh Padmanabhan Faisal Alsaif S. Senthilkumar |
| author_facet | K. Krishnaram T. Suresh Padmanabhan Faisal Alsaif S. Senthilkumar |
| author_sort | K. Krishnaram |
| collection | DOAJ |
| description | Abstract Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study. |
| first_indexed | 2024-04-24T12:40:35Z |
| format | Article |
| id | doaj.art-81003e0d667c4221965aa692b40c018c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T12:40:35Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-81003e0d667c4221965aa692b40c018c2024-04-07T11:15:59ZengNature PortfolioScientific Reports2045-23222024-04-0114111710.1038/s41598-024-58852-8Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applicationsK. Krishnaram0T. Suresh Padmanabhan1Faisal Alsaif2S. Senthilkumar3Department of EEE, E.G.S. Pillay Engineering CollegeDepartment of EEE, E.G.S. Pillay Engineering CollegeDepartment of Electrical Engineering, College of Engineering, King Saud UniversityDepartment of ECE, E.G.S. Pillay Engineering CollegeAbstract Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.https://doi.org/10.1038/s41598-024-58852-8E-vehiclesSolar systemInterleaved boost converter (ILBC)Maximum power point tracking (MPPT)Artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) |
| spellingShingle | K. Krishnaram T. Suresh Padmanabhan Faisal Alsaif S. Senthilkumar Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications Scientific Reports E-vehicles Solar system Interleaved boost converter (ILBC) Maximum power point tracking (MPPT) Artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) |
| title | Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications |
| title_full | Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications |
| title_fullStr | Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications |
| title_full_unstemmed | Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications |
| title_short | Performance optimization of interleaved boost converter with ANN supported adaptable stepped-scaled P&O based MPPT for solar powered applications |
| title_sort | performance optimization of interleaved boost converter with ann supported adaptable stepped scaled p o based mppt for solar powered applications |
| topic | E-vehicles Solar system Interleaved boost converter (ILBC) Maximum power point tracking (MPPT) Artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) |
| url | https://doi.org/10.1038/s41598-024-58852-8 |
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