This paper proposes a review of the main characteristics and parameters that have to considered modelling a photovoltaic module. For the study it will be left from the photovoltaic cell, fundamental element of the conversion of light to electrical current. Also, a photovoltaic model using Matlab, which can be representative of PV cell and module, is presented. The created model has been based on previous studies. With the information of those previous models, curves IV and PV have been calculated comparing the results to each other. Also, the MPP of all the models has been calculated. Finally, the model has been validated with experimental data of a commercial PV module, Mitsubishi PV-TD1185MF5.

This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

Proton exchange membrane fuel cell (PEMFC) topology is becoming one of the most reliable and promising alternative resource of energy for a wide range of applications. However, efficiency improvement and lifespan extension are needed to overcome the limited market of fuel cell technologies. In this paper, an efficient approach based on a super-twising algorithm (STA) is proposed for the PEMFC system. The control objective is to lengthen the fuel cell lifetime by improving its power quality, as well as to keep the system operating at an optimal and efficient power point. The algorithm adjusts the PEMFC operating point to the optimum power by tuning the duty cycle of the boost converter. The closed-loop system includes the Heliocentris hy-Expert^TM PEMFC, DC–DC boost converter, DSPACE DS1104, dedicated PC, and a programmable electronic load. The practical implementation of the proposed STA on a hardware setup is performed using a dSPACE real-time digital control platform. The data acquisition and the control system are conducted together with the dSPACE 1104 controller board. To demonstrate the performance of the proposed algorithm, experimental results are compared with 1-order sliding mode control (SMC) under different load resistance. The obtained results demonstrate the validity of the proposed control scheme by ensuring at least 72% of the maximum power produced by PEMFC. In addition, it is proven that the STA ensures all the fundamental properties of the 1-order SMC, as well as providing chattering reduction of 91%, which will ameliorate as a consequence the fuel cell lifetime.