Knowledge-Based Sensors for Controlling A High-Concentration Photovoltaic Tracker

Area of Science:

• Renewable Energy Engineering
• Control Systems
• Internet of Things

Background:

• High-concentration photovoltaic (HCPV) systems reduce semiconductor material costs by concentrating sunlight.
• Accurate tracking is crucial for HCPV efficiency, but real-world installations face imprecision.
• Existing tracking systems require improvement to address inaccuracies.

Purpose of the Study:

• To design and implement a knowledge-based controller for HCPV trackers.
• To enhance the precision and reliability of HCPV solar tracking systems.
• To leverage fuzzy rule-based systems (FRBS) and Internet of Things (IoT) for improved control.

Main Methods:

• Development of two knowledge-based controllers using fuzzy rule-based systems (FRBS).
• Implementation of controllers in a real HCPV system via Internet of Things (IoT) technology.
• Experimental validation of controller performance and identification of new sources of imprecision.

Main Results:

• A controller based on electrical current measurement demonstrated superior performance.
• FRBS effectively managed inaccuracy and uncertainty in the HCPV tracking system.
• New factors contributing to imprecision in real HCPV installations were identified.

Conclusions:

• Knowledge-based controllers, particularly those using FRBS and IoT, can significantly improve HCPV tracker accuracy.
• The electrical current-based controller is a promising approach for enhancing HCPV system efficiency.
• Addressing installation-specific uncertainties is key to optimizing HCPV performance.