Optimization of solar energy using artificial neural network controller with dc-dc boost, cuk, and single-ended primary inductor converter (SEPIC) converters

The challenge of the greenhouse effect today is to find ways to prevent CO2 emissions, as this harmful gas causes global negative changes. One eco-friendly energy source is solar power, which uses a solar array system composed of various components. A critical part of this system is the Maximum Power Point Tracker (MPPT), which ensures optimal power generation. The MPPT's signal is sent to an Insulated Gate Bipolar Transistor (IGBT) via a Pulse Width Modulator (PWM), adjusting the system's resistance. Traditional controllers used the Perturbation and Observation (P&O) algorithm, which struggled with rapid environmental changes. The new AI-based Artificial Neural Network (ANN) controller improves efficiency by instantly adapting to changes. This work compares the ANN controller with the use of three data sets of 104, 201, and 1001 with three DC-DC converters: Boost, Cuk, and Single-Ended Primary Inductor Converter (SEPIC) converters.