Deep neural network algorithm for MPPT control of double diode equation based PV module

Abstract

One of the most promising renewable energy sources (RES) is photovoltaic energy system. It generates power by using a photovoltaic (PV) module. The solar illumination and temperature value are the main parameters for the PV module because a small change can affect the entire power generation. The PV module is based on the double diode equation for extracting unknown parameter values and tracking maximum power point using basic analytical methods (Newton Raphson and Lambert W-function), particle swarm optimization (PSO) and artificial neural network (ANN) algorithms are performed. Then, a deep neural network (DNN) algorithm is proposed in this work for alleviating the disadvantages of the existing methods. Particularly, the DNN algorithm is proposed for reducing the mismatching power loss. Firstly, two-parameter values retrieve from a specific location at NASA open-access source for the year 2020. Then, the parameter values optimization through two analytical methods such as Newton Raphson (NR) and Lambert-W function (W-function) methods. Based on these analytical methods, the characteristic curve is executed. It is compared one among one and also the analytical methods evaluates with soft computing algorithms. The soft computing algorithm represents particle swarm optimization and the artificial neural network. Both the analytical methods and the soft computing algorithms compare for the extraction of the unknown parameter value for the PV module. Then, the existing algorithm is compared with advanced soft computing algorithms. In that deep neural network, the algorithm is implemented for the maximum power point tracking (MPPT) process. The DNN algorithm contains two inputs, three hidden layers and one output. The input neuron is defined with the voltage and current value and the hidden layer contains the sigmoid activation function. The result evaluates in terms of percentage of error for maximum power point (PEmpp).