The electrical energy from the sun can be extracted using solar photovoltaic (PV) modules. This energy can be maximized if the connected load resistance matches that of the PV panel. In search of the optimum matching between the PV and the load resistance, the maximum power point tracking (MPPT) technique offers considerable potential. This paper aims to show how the modelling process of an efficient PV system with a DC load can be achieved using a fuzzy neural network (FNN) controller. This is applied via an innovative methodology, which senses the irradiance and temperature of the PV panel and produces an optimal value of duty ration for the boost converter to obtain the MPPT. The coefficients of this controller have been refined based upon previous data sets using the irradiance and temperature. A gradient descent algorithm is employed to improve the parameters of the FNN controller to achieve an optimal response. The validity of the PV system using the MPPT technique based on the FNN controller is further demonstrated via a series of experimental tests at different ambient conditions. The simulation results show how the MPPT technique based on the FNN controller is more effective in maintaining the optimal power values compared with conventional techniques.
Reconfigurable beam steering using circular disc microstrip patch antenna with a ring slot is proposed. The overall dimension of the antenna is 5.4 × 5.4 mm 2 printed on 0.504 mm thick, RT5870 substrate with relative permittivity 2.3 and loss tangent 0.0012. The designed antenna operates at the expected 60 GHz 5G frequency band with a central coaxial probe feed. Two NMOS switches are utilized to generate three different beam patterns. Activating each switch individually results in a 70° shift in the main beam direction with constant frequency characteristics. The power gain is 3.9-4.8 dB in the three states of switch configurations. Simulated results in terms of return loss, peak gains and radiation pattern are presented and show good performance at the expected 60 GHz band for 5G applications.
Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.
<span lang="EN-US">This paper deals with voltage tracking control of DC- DC boost converter based on Fuzzy neural network. Maintaining the output voltage of the boost converter in some applications are very important, especially for sudden change in the load or disturbance in the input voltage. Traditional control methods usually have some disadvantages in eliminating these disturbances, as the speed of response to these changes is slow and thus affect the regularity of the output voltage of the converter. The strategy is to sense the output voltage across the load and compare it with the reference voltage to ensure that it follows the required reference voltages. In this research, fuzzy neural was introduced to achieve the purpose of voltage tracking by training the parameter of controller based on previous data. These data sets are the sensing input voltage of the converter and the value of the output load changes. To establish the performance of proposed method, MATLAB/SIMULINK environments are presented, simulation results shows that proposed method works more precisely, faster in response and elimination the disturbances</span>
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