Committee Chair

Ofoli, Abdul

Committee Member

Ahmed, Raga; Reising, Donald

Department

Dept. of Electrical Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

The number of utility-scale PV installations is rising, with a power capacity of 12.5 Gigawatts installed in 2021, 10.4 in 2022, and an estimated 24 Gigawatts installed in 2023 [1]. With larger-scale installations, quicker ways of identifying and locating damaged PV arrays are needed. The solution presented in this thesis is to use drones to capture aerial photos and TensorFlow-Lite and Keras deep learning methods to determine if a panel has defects, such as debris, cracked panels, and hotspots. The model features an execution time of 0.185 seconds per picture. In addition, the model will run on an embedded system with a relatively low impact on power consumption, minimizing the reduction of flight time. The Raspberry Pi has an approx. 0.1minute effect on flight time while idling and with the worst-case scenario of affecting flight time by approximately two minutes if left running for the entire flight.

Acknowledgments

I would like to express my thanks to Dr. Ofoli for his guidance and the opportunity to work on this endeavor. Dr. Ahmed and Dr. Reising, thank you for your guidance throughout the year and for serving on the thesis commi ee on the short turnaround of this venture. The SMART Scholarship and Arnold Air Force Base for allowing me to work and be a student at the same me.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

8-2024

Subject

Deep learning (Machine learning); Electric power production--Measurement; Photovoltaic power systems; Raspberry Pi (Computer); Solar panels--Maintenance and repair

Keyword

TensorFlow; K-Fold Cross Validation; TensorFlow-Lite; CNN; PV; Solar Panels

Document Type

Masters theses

DCMI Type

Text

Extent

xiv, 85 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

Additional Files
thesis library.enl (32 kB)
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