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• W2980283181 abstract "In this paper, Artificial Neural Network (ANN) optimization with Genetic Algorithm (GA) is implemented. The optimized training to ANN is provide using Bayesian regulation. For this study, a Photovoltaic (PV) system has considered and optimal power tracking been interpreted with proper adjustment of ANN weights using GA approach, which reduces the Root Mean Square Error (RMSE). In this work, the single-ended primary-inductor converter (SEPIC) has been utilized for better power tracking from PV modules. SEPIC Converter accomplish with impedance matching power device and provides utmost PV power tracking. Space vector pulse width modulation-dSPACE interface been utilized as an inverter control. Simulated responses show the potency of the proposed system under sag, swell and varying loading conditions." @default.
• W2980283181 created "2019-10-18" @default.
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• W2980283181 date "2019-04-01" @default.
• W2980283181 modified "2023-09-27" @default.
• W2980283181 title "An AN-GA Controlled SEPIC Converter for Photovoltaic Grid Integration" @default.
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• W2980283181 doi "https://doi.org/10.1109/cpe.2019.8862395" @default.
• W2980283181 hasPublicationYear "2019" @default.
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