Implementation of radial basis function network-based maximum power point tracking for a PV-fed high step-up converter - Bharathidasan, Mohan; Indragandhi, Vairavasundaram (Frontiers in Energy Research, 2022)
This research offers a Maximum Power Point Tracking (MPPT) algorithm for photovoltaic (PV) systems based on Neural Networks (NN) and a rapid step-up converter configuration. An improved variable step size-Radial Basis Function Network (RBFN) in NN algorithm is accomplished in the proposed system to track the Maximum Power Point (MPP) with high convergence speed and obtain maximum power with reduced oscillations. Under various irradiance and temperature conditions, the performance of the recommended algorithm was compared to that of Particle Swarm Optimization (PSO), Modified Perturb and Observe (P&O) MPPT technique, Artificial Neural Network (ANN), Multilayer Perceptron Feed-forward (MPF) NN based MPPT method. In this system, new interleaved non-isolated large step-up converter with coupled inductor technique is suggested to compensate the discord in PV devices to enable for a continuous and independent power flow. The proposed PV fed converter system are validated under partial shading conditions (PSC's) and uniform of solar PV and the results are experimentally verified with the use of a programmable direct current (DC) source. The results obtained indicates that the proposed converter produces output with high gain, continuous input current, the low voltage stress on switches, minimal ripple, high power density, and extensive input and output operations. Finally, a prototype has been implemented to verify the functionality of the presented converter in continuous conduction mode operation with an input voltage range of 20 V and an output voltage of 200 V.