Comparative assessment of various MPPT algorithms for solar photovoltaic systems under dynamic shading conditions
In order to guarantee grid stability and efficiently manage energy resources, there has been a substantial energy transition brought about by growing worries about resource depletion, climate change, and environmental pollution. Energy is the basic requirement for the sustainable development of huma...
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Springer Science and Business Media Deutschland GmbH
2025
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| Summary: | In order to guarantee grid stability and efficiently manage energy resources, there has been a substantial energy transition brought about by growing worries about resource depletion, climate change, and environmental pollution. Energy is the basic requirement for the sustainable development of human society. Conventional energy resources are limited and causing environmental disturbances consistently. Renewable energy offers better alternatives to tackle current energy crisis. A significant proportion of power generated in India is now coming from solar PV systems. However, due to its intermittence nature, efficient MPPT algorithm becomes necessity to extract maximum output power from the system to enhance its efficiency. Therefore, we have proposed a novel neural network based MPPT algorithm and presents a comparative analysis with conventional MPPT techniques ?Perturb and Observe?, and ?Incremental Conductance? based techniques. The experimental outcomes are validated which shows neural network based MPPT algorithm provides better performance characteristics like reduced steady state error compared to conventional techniques. The parameters considered for comparison are rise time, settling time and power output under different environmental conditions such as irradiance and temperature. Finally, the proposed MPPT controller?s performance is evaluated in MATLAB environment and simulation results show the proposed scheme?s superiority as compared to conventional control techniques under different operating conditions. ? The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. |
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