Abstract
In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.
Highlights
Since the power capacity by a generator is relatively low and cost’s energy is relatively high; we will have a strong demand to increase the efficiency in order to reduce the energy production cost of photovoltaic and its optimization
The maximum power point tracking (MPPT) method of Perturbation and Observation perturbation and observation (P&O) is based on the property that the derivative of the power-voltage characteristic of the photovoltaic module or cell is positive on the left side of the maximum power point, negative on its right side, and zero at the Maximum Power Point (MPP). [ and ) are respectively the output power and voltage of the PV module or cell
We tested and validated our hybrid MPPT technique based on the Artificial Neuron Network using the Matlab / Simulink modeling and simulation tool considering the variations of solar irradiation and ambient temperature, the results are illustrated on Figures 7 to 10
Summary
Since the power capacity by a generator is relatively low and cost’s energy is relatively high; we will have a strong demand to increase the efficiency in order to reduce the energy production cost of photovoltaic and its optimization. The operating point oscillates near the Maximum Power Point (MPP), which leads to a loss energy and a tracking time relatively long, and, the operating point changes curve due variations climatic conditions (solar irradiation and ambient temperature) [3]. To avoid using a derivative to perform the maximal power point process, the derivative used in the perturbation & observation method to detect the position of the MPP is replaced in the Ripple Correlation Control (RCC) method by a correlation function [11] Another MPPT technique, maximum seek to control (ESC) having a self-optimization strategy is studied by Reisi et al [12], it has the same operating principle that RCC MPPT. The performance of our MPPT method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink modeling and simulation tool and to test and valid the proposed neural networks
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