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Solar panel damage identification using tensorflow lite

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Title: Solar panel damage identification using tensorflow lite
Authors: Muncie, Garrick
Committee Members: Ofoli, Abdul; Ahmed, Raga; Reising, Donald; College of Engineering and Computer Science
Summary: 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.
URL: https://scholar.utc.edu/theses/871
Database: OpenDissertations
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Header DbId: ddu
DbLabel: OpenDissertations
An: ddu.oai.scholar.utc.edu.theses.2049
AccessLevel: 6
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 0
IllustrationInfo
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  Label: Title
  Group: Ti
  Data: Solar panel damage identification using tensorflow lite
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Muncie%2C+Garrick%22">Muncie, Garrick</searchLink>
– Name: Author
  Label: Committee Members
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  Data: <searchLink fieldCode="CO" term="%22Ofoli%2C+Abdul%22">Ofoli, Abdul</searchLink>; <searchLink fieldCode="CO" term="%22Ahmed%2C+Raga%22">Ahmed, Raga</searchLink>; <searchLink fieldCode="CO" term="%22Reising%2C+Donald%22">Reising, Donald</searchLink>; <searchLink fieldCode="CO" term="%22College+of+Engineering+and+Computer+Science%22">College of Engineering and Computer Science</searchLink>
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  Label: Summary
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  Data: 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.
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Deep learning (Machine learning)
        Type: general
      – SubjectFull: Electric power production--Measurement
        Type: general
      – SubjectFull: Photovoltaic power systems
        Type: general
      – SubjectFull: Raspberry Pi (Computer)
        Type: general
      – SubjectFull: Solar panels--Maintenance and repair
        Type: general
    Titles:
      – TitleFull: Solar panel damage identification using tensorflow lite
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Muncie, Garrick
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      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Type: published
              Y: 2024
ResultId 1