Internet of Things Based Horizontal Axis Tracking Solar Panel Performance Evaluation

Abstract

Renewable energy is undergoing significant advancements through the utilization of new technologies. Among these technologies, fixed solar panels serve as a fundamental type of solar photovoltaic energy generator. Monitoring and optimizing the performance of these panels is achieved through the implementation of controller algorithms. However, there is a growing emphasis on axis tracking systems for solar panels, as that has the feasible to extract more power compared to fixed panels. In this context, the integration of the Internet of Things (IoT), big data analytics, deep learning, and cloud computing presents a contemporary solution for addressing various challenges associated with monitoring renewable solar energy systems. By employing sensors and actuators grouped within an IoT framework, real-time data can be extracted and stored in a cloud server, allowing remote access from anywhere. Furthermore, the use of meta-heuristic optimization algorithms, such as the Whale Optimization Algorithm, has become commonplace in solving engineering issues. In this proposed system, the performance of horizontal axis trackers is analyzed and compared with fixed panels using the WOA-based axis tracking analysis. Through the application of a control algorithm, optimal values can be obtained under typical weather conditions. The system’s simulation and performance are thoroughly assessed and evaluated.