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Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

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This paper shows the details of the design of DC-DC Boost Converter as the Maximum Power Point Tracker (MPPT). This technique is used to optimize the potential of energy generated by photovoltaics. The changing of the duty cycle in the switching Boost Converter process will affect the amount of photovoltaic (PV) power output. Boost Converter is chosen because the output voltage of PV arrays is smaller than the DC source storage one. In this study, the Artificial Neural Network (ANN) algorithm has been applied as a method to search as Maximum Power Point (MPP). ANN learning data has been used to adjust the duty cycle of Boost Converter so that the Boost Converter output voltage and current change and the output power reaches MPP, which is the target data. The design results have been implemented using 3 pieces of PV, each one having 100WP of power. The experimental results show that the proposed method can optimize PV output power for DC source storage with an average power increase of 56.22% compared to PV output power without using MPPT control.
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ANN Algorithm; Boost Converter; MPPT; Photovoltaic

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