Abstract
As fossil fuels are getting depleted it is very much essential to harvest solar energy. The harvesting and conversion methods available in literature are simulation-based. A very few work has been reported using hardware circuitry. However, machine learning-based DSP controller for solar energy harvesting is not available in literature. In the proposed study, machine learning-based DSP controller is implemented. The genetic algorithm (GA)-based DSP controller has been designed for enhancing the efficiency of solar PV. In the proposed work, perturb and observe (P&O) technique and genetic algorithm (GA) have been considered to achieve maximum power point and precise control parameters of PID controller respectively. Single DSPTMS320F28377s has been used to implement both P&O and GA and it is revealed that the proposed DSP-based hardware model provides better speed, efficiency and reliability than the existing simulation-based controller. The proposed work will bring a paradigm shift in solar energy harvesting and control.
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