RESUMEN
To reduce the cost of generated electrical energy, high-concentration photovoltaic systems have been proposed to reduce the amount of semiconductor material needed by concentrating sunlight using lenses and mirrors. Due to the concentration of energy, the use of tracker or pointing systems is necessary in order to obtain the desired amount of electrical energy. However, a high degree of inaccuracy and imprecision is observed in the real installation of concentration photovoltaic systems. The main objective of this work is to design a knowledge-based controller for a high-concentration photovoltaic system (HCPV) tracker. The methodology proposed consists of using fuzzy rule-based systems (FRBS) and to implement the controller in a real system by means of Internet of Things (IoT) technologies. FRBS have demonstrated correct adaptation to problems having a high degree of inaccuracy and uncertainty, and IoT technology allows use of constrained resource devices, cloud computer architecture, and a platform to store and monitor the data obtained. As a result, two knowledge-based controllers are presented in this paper: the first based on a pointing device and the second based on the measure of the electrical current generated, which showed the best performance in the experiments carried out. New factors that increase imprecision and uncertainty in HCPV solar tracker installations are presented in the experiments carried out in the real installation.
RESUMEN
The demand of electric power consumption is increasing very rapidly worldwide and to fulfill this requirement, solar energy is one of the most viable solution as renewable energy source. Photovoltaic (PV) cell based sun-tracker system (STS) produces maximum current when sunlight vertically incident on its surface. Hence, there is a need of optimized continuous axis position control of STS to achieve maximum output current. This task can be done on the basis of the fuzzy control system. Usually, in the traditional fuzzy control system (FCS), tuning of designed fuzzy parameter is done by trial and error method. However, this type of FCS parameter tuning approach may or may not give optimal solution. Thus, in presented work, an optimal tuning technique with Takagi, Sugeno and Kang (TSK) fuzzy controller (TFC) using Gray Wolf Optimization (GWO) for STS has been proposed. In order to validate the proposed work, different objective functions have been employed to carry out fuzzy controller parameter optimization. A comparative analysis has been performed on the basis of three parameters: settling time, maximum-overshoot and optimal fuzzy parameter on different constrain set. The results obtained with the GWO optimization algorithm were also compared with other popular population algorithms, i.e. Whale Optimization Technique (WOT) and Particle Swarm Optimization (PSO) algorithms.