Abstract
This paper presents the application of the Adaptive Neuro Fuzzy Inference System (ANFIS) to track the maximum power of a photovoltaic generator that feeds a motor-pump group unit through a Pulse Width Modulation (PWM) inverter powered by a Single Ended Primary Inductance Converter (SEPIC) installed in the laboratory. The ANFIS control is trained in different temperatures and irradiances and the maximum power point tracking system varies automatically the duty cycle of the SEPIC converter. The performance of the MPPT controller is tested in simulations in Matlab/Simulink.
Highlights
In photovoltaic (PV) systems, sunlight is converted into DC electricity
This paper addresses the above problem and presents the Adaptive Neuro Fuzzy Inference System (ANFIS) method used as the MPPT algorithm from which are many presented in previous papers [1]
The ANFIS surface depicts that the maximum available power point of solar PV module increases with increase in irradiance and moderate temperature which verifies the non
Summary
In photovoltaic (PV) systems, sunlight is converted into DC electricity. From the PV module we get low voltage, so we have to increase this voltage using an SEPIC converter. The MPPT of the PV system using SEPIC converter is controlled by intelligent controllers, in our case the developed Adaptive Neuro Fuzzy Inference System (ANFIS) model. This paper addresses the above problem and presents the ANFIS method used as the MPPT algorithm from which are many presented in previous papers [1]. Many papers focused on the comparison of the ANFIS with some other Artificial Intelligence (AI) methods such as Neural Networks (NNs), Fuzzy Logic (FL) and proved that ANFIS is the most suitable for use in uncertain systems [2] and without a doubt presented ANFIS as the most suitable algorithm for MPP tracking
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