Comparison between Adaptive Neuro Fuzzy inference system ANFIS based MPPT controller and ANN based MPPT controller for stand alone solar energy generation system.

Electrical Engineering

In this paper, an ANFIS-based MPPT controller for a DC-DC converter is developed and its performance is analyzed. There are two power sources, a DC-to-DC booster converter, and a load in the proposed system. The proposed algorithm utilizes neural networks and fuzzy logic. The MPPT controllers use Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance converter performance. As a result of high oscillations in conventional algorithms such as Perturb and Observe (P&O), the paper proposes AI-based methods for enhancing efficiency. ANFIS is more efficient in tracking MPP than ANN-based controllers with fewer settling times, overshoots, oscillations, and delays. To demonstrate the ANFIS controller's ability to follow reference voltages and currents, its performance is simulated and compared with the MPPT controller based on artificial intelligence techniques. The simulation results show that MPPT controllers based on ANFIS provide better performance than MPPT controllers based on Artificial Intelligence Techniques under various operating conditions.

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