Author:
Bharathidasan Mohan,Indragandhi Vairavasundaram
Abstract
This research offers a maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems based on neural networks (NNs) and a rapid step-up converter configuration. An improved variable step size-radial basis function network (RBFN) in the 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), and multilayer perceptron feed-forward (MPF) NN-based MPPT method. In this system, a new interleaved non-isolated large step-up converter with the coupled inductor technique is suggested to compensate for the discord in PV devices to enable a continuous and independent power flow. The proposed PV-fed converter system is validated under partial shading conditions (PSCs) and uniform solar PV, and the results are experimentally verified with the use of a programmable direct current (DC) source. The obtained results indicate that the proposed converter produces output with high gain, continuous input current, 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.
Subject
Economics and Econometrics,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献