研究目的
To design a knowledge-based controller for a high-concentration photovoltaic system (HCPV) tracker to reduce the cost of generated electrical energy by concentrating sunlight using lenses and mirrors, addressing the high degree of inaccuracy and imprecision observed in real installations.
研究成果
The controller based on the FRBS sensor and an electrical current probe showed the best performance, obtaining values similar to those obtained in the module characterization without requiring calibration. The study concludes that IoT technologies are effective for executing HCPV controllers and monitoring the evolution of variables, and it identifies additional factors contributing to imprecision and uncertainty in HCPV installations. Future work includes using an IoT fog computing architecture, characterizing different HCPV modules, and comparing other controllers.
研究不足
The study identifies several factors that increase imprecision and uncertainty in HCPV solar tracker installations, including inaccuracy in the manufacturing process of HCPV modules, precision errors in the manufacture and installation of the tracker structure, and minimum movement of the electrical engines. The controller based on the FRBS sensor with a pointing device requires perfect calibration, and the ephemeris algorithm-based controller accumulates azimuth and elevation errors.
1：Experimental Design and Method Selection
The methodology proposed consists of using fuzzy rule-based systems (FRBS) and implementing the controller in a real system by means of Internet of Things (IoT) technologies. The design includes two knowledge-based controllers: one based on a pointing device and the other on the measure of the electrical current generated.
2：Sample Selection and Data Sources
The experiments were carried out on a real two-axis tracker with HCPV modules, using data from the tracker's position, electrical current generated, and environmental conditions.
3：List of Experimental Equipment and Materials
The tracker is composed of a metal structure, HCPV modules, a calibrated solar cell, DC azimuth and elevation motors, encoders, a pointing error sensor, electrical current-generated sensors, and a control system based on a low-cost 32-bit microcontroller.
4：Experimental Procedures and Operational Workflow
The control system calculates the elevation and azimuth angles to be performed at each moment using the state of the system. The error inferred by the FRBS system is added to the calculated angles. The angular movements are verified by encoders, and data are sent to an IoT cloud platform for storage and analysis.
5：Data Analysis Methods
The performance of the controllers was evaluated based on the electrical current generated by the HCPV module and the pointing errors measured by a precision instrument. The Isc/DNI ratio was used to normalize and compare the currents generated on different days.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
