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A p-n junction is formed when p-type and n-type semiconductor materials are joined together. At the interface of the p-n junction, holes from the p-side and electrons from the n-side begin to diffuse into the opposite sides due to the concentration gradient. This diffusion of carriers leads to a region around the junction where there are no free charge carriers, known as the depletion region. The charge density within the depletion region for the n-side and p-side can be described by the...

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

Joaquin Canada-Bago¹, Jose-Angel Fernandez-Prieto¹, Manuel-Angel Gadeo-Martos¹

¹Telematic Engineering System Research Group, CEATIC Center of Advanced Studies in Information and Communication Technologies, University of Jaén, Campus Las Lagunillas, C.P. 23071 Jaén, Spain.

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|March 4, 2020

Summary

This study designed a knowledge-based controller for high-concentration photovoltaic (HCPV) trackers using fuzzy logic and IoT. The controller improved accuracy in real-world installations, reducing energy costs.

Keywords:

Internet of Things high-concentration photovoltaic systems knowledge-based sensor sun tracker

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Published on: July 18, 2015

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.