Modeling of a photovoltaic array in MATLAB simulink and maximum power point tracking using neural network

Abstract

Maximum Power Point Tracking (MPPT) is very useful tool in PV application. Solar radiation and temperature are the main factor for which the electric power supplied by a photovoltaic system varies. The voltage at which PV module can produce maximum power is called ‘maximum power point’ (or peak power voltage).1–3 The main principle of MPPT is responsible for extracting the maximum possible power from the photovoltaic and feed it to the load via dc to dc converter which steps up/steps down the voltage to required magnitude. Various MPPT techniques have been used in past but Perturb & Observe (P&O) algorithm is most widely accepted.4–6 P&O algorithm has also been shown to provide wrong tracking with rapidly varying irradiance.7–10 In this paper we are implemented neural network based MPPT method. Artificial Neural Network (ANN) is an artificial network that can able to mimic the human biological neural networks behavior. ANN widely used in modeling complex relationships between inputs and outputs in nonlinear systems. ANN can also be defined as parallel distributed information processing structure. The ANN consists of inputs, and at least one hidden layer and one output layer. These layers have processing elements which are called neurons interconnected together. To calculate error contribution of each neuron after a batch of data processing a method called ‘back propagation’ is used. Back propagation is commonly used by the gradient descent optimization algorithm to adjust the weight of neurons by calculating the gradient of the loss function. This technique is also called back propagation error. This is because the error is calculated at the output and circulated back through the network layers.11 Mathematical solar array modeling