Binary Classification of Defective Solar PV Modules Using Thermography

Kamran Ali Khan Niazi; Wajahat  Akhtar; Hassan Abbas  Khan; Sarmad  Sohaib; Ahmad Kamal  Nasir

Binary Classification of Defective Solar PV Modules Using Thermography

Kamran Ali Khan Niazi, Wajahat Akhtar, Hassan Abbas Khan, Sarmad Sohaib, Ahmad Kamal Nasir

Research output: Contribution to book/anthology/report/conference proceeding › Conference abstract in proceeding › Research › peer-review

17 Citations (Scopus)

393 Downloads (Pure)

Abstract

Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this paper, we categorize operational solar panels into two categories (Defective and Non-Defective panels) using a machine learning technique i.e. texture features through thermography assessment. Further, the panels are also categorized for diagnostic perspective using nBayes classifier. Results from an investigation for a 42.24 kWp PV system showed a mean recognition rate of 98.4% for a set of 260 test samples.

Original language	English
Title of host publication	Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)
Number of pages	5
Publication date	Jun 2018
Pages	0753-0757
ISBN (Print)	978-1-5386-8530-3
ISBN (Electronic)	978-1-5386-8529-7
Publication status	Published - Jun 2018
Event	7th World Conference on Photovoltaic Energy Conversion - Hilton Waikoloa Village, Waikoloa, United States Duration: 10 Jun 2018 → 15 Jun 2018 http://www.wcpec7.org/WCPEC-7/

Conference

Conference	7th World Conference on Photovoltaic Energy Conversion
Location	Hilton Waikoloa Village
Country/Territory	United States
City	Waikoloa
Period	10/06/2018 → 15/06/2018
Internet address	http://www.wcpec7.org/WCPEC-7/

Keywords

PV solar panel
thermal imaging (TI)
hotspots machine learning
nBayes classifier

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https://ieeexplore.ieee.org/document/8548138

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Niazi, K. A. K., Akhtar, W., Khan, H. A., Sohaib, S., & Nasir, A. K. (2018). Binary Classification of Defective Solar PV Modules Using Thermography. In Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) (pp. 0753-0757) https://ieeexplore.ieee.org/document/8548138

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title = "Binary Classification of Defective Solar PV Modules Using Thermography",

abstract = "Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this paper, we categorize operational solar panels into two categories (Defective and Non-Defective panels) using a machine learning technique i.e. texture features through thermography assessment. Further, the panels are also categorized for diagnostic perspective using nBayes classifier. Results from an investigation for a 42.24 kWp PV system showed a mean recognition rate of 98.4% for a set of 260 test samples.",

keywords = "PV solar panel, thermal imaging (TI), hotspots machine learning, nBayes classifier",

author = "Niazi, {Kamran Ali Khan} and Wajahat Akhtar and Khan, {Hassan Abbas} and Sarmad Sohaib and Nasir, {Ahmad Kamal}",

year = "2018",

month = jun,

language = "English",

isbn = "978-1-5386-8530-3",

pages = "0753--0757",

booktitle = "Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)",

note = "7th World Conference on Photovoltaic Energy Conversion , WCPEC-7 ; Conference date: 10-06-2018 Through 15-06-2018",

url = "http://www.wcpec7.org/WCPEC-7/",

}

Niazi, KAK, Akhtar, W, Khan, HA, Sohaib, S & Nasir, AK 2018, Binary Classification of Defective Solar PV Modules Using Thermography. in Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC). pp. 0753-0757, 7th World Conference on Photovoltaic Energy Conversion , Waikoloa, Hawaii, United States, 10/06/2018. <https://ieeexplore.ieee.org/document/8548138>

Binary Classification of Defective Solar PV Modules Using Thermography. / Niazi, Kamran Ali Khan; Akhtar, Wajahat ; Khan, Hassan Abbas et al.
Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC). 2018. p. 0753-0757.

Research output: Contribution to book/anthology/report/conference proceeding › Conference abstract in proceeding › Research › peer-review

TY - ABST

T1 - Binary Classification of Defective Solar PV Modules Using Thermography

AU - Niazi, Kamran Ali Khan

AU - Akhtar, Wajahat

AU - Khan, Hassan Abbas

AU - Sohaib, Sarmad

AU - Nasir, Ahmad Kamal

PY - 2018/6

Y1 - 2018/6

N2 - Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this paper, we categorize operational solar panels into two categories (Defective and Non-Defective panels) using a machine learning technique i.e. texture features through thermography assessment. Further, the panels are also categorized for diagnostic perspective using nBayes classifier. Results from an investigation for a 42.24 kWp PV system showed a mean recognition rate of 98.4% for a set of 260 test samples.

AB - Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this paper, we categorize operational solar panels into two categories (Defective and Non-Defective panels) using a machine learning technique i.e. texture features through thermography assessment. Further, the panels are also categorized for diagnostic perspective using nBayes classifier. Results from an investigation for a 42.24 kWp PV system showed a mean recognition rate of 98.4% for a set of 260 test samples.

KW - PV solar panel

KW - thermal imaging (TI)

KW - hotspots machine learning

KW - nBayes classifier

M3 - Conference abstract in proceeding

SN - 978-1-5386-8530-3

SP - 753

EP - 757

BT - Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)

T2 - 7th World Conference on Photovoltaic Energy Conversion

Y2 - 10 June 2018 through 15 June 2018

ER -

Binary Classification of Defective Solar PV Modules Using Thermography

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