Enhanced the Maximum Power Point Tracking (MPPT) of Photovoltaic Systems Using the Flying Squirrel Search Optimization (FSSO) algorithm and Feed Forward Neural Network
DOI:
https://doi.org/10.31663/utjes.14.1.645Keywords:
Maximum Power Point (MPP), Photovoltaic System, Artificial Intelligence, ANN, The Flying Squirrel Search Optimization (FSSO) algorithmAbstract
In this current Investigation, a solar PV system is applied. The Flying Squirrel Search Optimization (FSSO) algorithm for Maximum Power Point Tracking (MPPT) is examined. The flying squirrel's movement serves as inspiration for the FSSO algorithm, which simulates their movements and evaluates fitness to find the optimal voltage and current levels for maximum power output. Through dynamic positioning of "squirrels" (potential solutions), the algorithm strives to find the optimal voltage and current combination, hence optimizing the PV system's total efficiency. The MATLAB script that is included demonstrates the unique optimization method of the FSSO algorithm for MPPT in a solar PV system. The parameters and movement rules of the script can be customized to match the unique features of the PV system, offering application versatility. In general, these methods raise. Before applying the FSSO algorithm, ANN was applied to aid in enhancing MPPT's performance by creating an input-output model. ANN results are based on 8761 datasets collected from (www.kaggle.com). ANN resulted in the best validation performance obtained at epoch 30, which is a total of 36 epochs. The coefficient of determination (R-squared) for the linear regression between the predictions of the neural network and the actual target values is displayed as a regression equal to 0.84666. The results concluded that the neural network has reasonably good predictive performance. Based on these results, applying FSSO using 100 iterations, the results concluded that the maximum power point of tracking was accurately determined based on photovoltaic resources.
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