{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2049"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2049","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Solar panel damage identification using tensorflow lite","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Muncie, Garrick"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ofoli, Abdul","Ahmed, Raga; Reising, Donald","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:47:13Z","subjects":["Deep learning (Machine learning)","Electric power production--Measurement","Photovoltaic power systems","Raspberry Pi (Computer)","Solar panels--Maintenance and repair"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/871","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ofoli, Abdul","Ahmed, Raga; Reising, Donald","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Muncie, Garrick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-08-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning (Machine learning)","Electric power production--Measurement","Photovoltaic power systems","Raspberry Pi (Computer)","Solar panels--Maintenance and repair"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/871"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Electrical Engineering","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."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Solar panel damage identification using tensorflow lite"]}]}],"canonical_facts":{"dc:contributor":["Ofoli, Abdul","Ahmed, Raga; Reising, Donald","College of Engineering and Computer Science"],"dc:creator":["Muncie, Garrick"],"dc:date":["2024-08-01T07:00:00Z"],"dc:description":["Dept. of Electrical Engineering","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."],"dc:description.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."],"dc:identifier":["https://scholar.utc.edu/theses/871"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Deep learning (Machine learning)","Electric power production--Measurement","Photovoltaic power systems","Raspberry Pi (Computer)","Solar panels--Maintenance and repair"],"dc:title":["Solar panel damage identification using tensorflow lite"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:13Z"}