An Artificial Intelligence-Based Hybrid MPPT Technique for SEPIC Converter Applied to Hybrid Renewable Energy Systems with Battery Storage

Abstract

This paper shows a Single Input Primary Inductor Converter (SEPIC) for a Hybrid Solar Wind System (HSWS) for a DC load using a battery energy storage simulation model. The intermittent and weather-dependent output voltage of hybrid solar-wind technology is an inherent disadvantage. To address this issue, it is possible to use a DC-DC converter with an MPPT control algorithm to build interfaces between an HSWS and a DC load. Separate converters are required for solar and wind power systems. To maintain a constant voltage at the DC connection, an interleaved 3-phase bidirectional DC-DC buck-boost converter, and a lead-acid battery bank are used. An Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimisation (ANFIS-PSO) AI-based MPPT control strategy is used to control the solar-wind system in order to maximise its output. Sinusoidal Pulse Width Modulation (SPWM) is employed to create gating pulses for the converter's switches. Using MATLAB/SIMULINK software, an ANFIS-PSO MPPT-based converter for a DC load is simulated, and the performance of the system is examined under various load scenarios.