Cascaded Artificial Neural Network Based MPPT Algorithm for PV Application

The capacity to reap the highest output power in various environmental conditions is one of the most critical tasks in the application of photovoltaic (PV) systems. Although many cutting-edge methods have been developed to accomplish this, the majority of methods have significant drawbacks, for instance, poor tracking capabilities and heavy computational load. Therefore, the aim of this work is to present a control algorithm that takes into account the connection between the solar array output power and the controller's PWM duty cycle of the MPPT boost converter. The proposed customized CNN is implemented in MATLAB/SIMULINK and compared with well known for its performance. The findings demonstrate an increase in the PV system's ability to generate power in any weather, as well as a reduction in the effects of rapid changes in solar irradiation on output power.

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Cascaded Artificial Neural Network Based MPPT Algorithm for PV Application

Cited by 2 publications

References 9 publications

IoT-integrated smart energy management system with enhanced ANN controller for small-scale microgrid

IoT-integrated smart energy management system with enhanced ANN controller for small-scale microgrid

Performance Analysis of MPPT Algorithms Designed for Photovoltaic System

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