Novel Photovoltaic Hot-spotting Fault Detection Algorithm

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, a novel photovoltaic (PV) hot-spotting fault detection algorithm is presented. The algorithm is implemented using the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. The evaluation of the hot-spots is analyzed based on the cumulative density function (CDF) modeling technique, whereas the percentage of power loss (PPL) and PV degradation rate are used to categorize the hot-spots into eight different categories. Next, the implemented CDF models are used to predict possible PV hot-spots affecting the PV modules. The developed algorithm is evaluated using three different PV modules affected by three different hot-spots. Remarkably, the proposed CDF models precisely categorize the PV hot-spots with a high rate of accuracy of almost above 80%.

Original languageEnglish
Article number8685128
Pages (from-to)378-386
Number of pages9
JournalIEEE Transactions on Device and Materials Reliability
Volume19
Issue number2
Early online date11 Apr 2019
DOIs
Publication statusPublished - 5 Jun 2019

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Fault detection
Probability density function
Polysilicon
Degradation

Cite this

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title = "Novel Photovoltaic Hot-spotting Fault Detection Algorithm",
abstract = "In this paper, a novel photovoltaic (PV) hot-spotting fault detection algorithm is presented. The algorithm is implemented using the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. The evaluation of the hot-spots is analyzed based on the cumulative density function (CDF) modeling technique, whereas the percentage of power loss (PPL) and PV degradation rate are used to categorize the hot-spots into eight different categories. Next, the implemented CDF models are used to predict possible PV hot-spots affecting the PV modules. The developed algorithm is evaluated using three different PV modules affected by three different hot-spots. Remarkably, the proposed CDF models precisely categorize the PV hot-spots with a high rate of accuracy of almost above 80{\%}.",
keywords = "Photovoltaic, Solar Cell, Hot-spots, PV hot spot detection, Fault Detection, Statistical analysis, thermal analysis, Solar energy, CDF modelling, solar energy, hot-spots, performance ratio, power loss",
author = "Mahmoud Dhimish and Peter Mather and Violeta Holmes",
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Novel Photovoltaic Hot-spotting Fault Detection Algorithm. / Dhimish, Mahmoud; Mather, Peter; Holmes, Violeta.

In: IEEE Transactions on Device and Materials Reliability, Vol. 19, No. 2, 8685128, 05.06.2019, p. 378-386.

Research output: Contribution to journalArticle

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T1 - Novel Photovoltaic Hot-spotting Fault Detection Algorithm

AU - Dhimish, Mahmoud

AU - Mather, Peter

AU - Holmes, Violeta

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AB - In this paper, a novel photovoltaic (PV) hot-spotting fault detection algorithm is presented. The algorithm is implemented using the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. The evaluation of the hot-spots is analyzed based on the cumulative density function (CDF) modeling technique, whereas the percentage of power loss (PPL) and PV degradation rate are used to categorize the hot-spots into eight different categories. Next, the implemented CDF models are used to predict possible PV hot-spots affecting the PV modules. The developed algorithm is evaluated using three different PV modules affected by three different hot-spots. Remarkably, the proposed CDF models precisely categorize the PV hot-spots with a high rate of accuracy of almost above 80%.

KW - Photovoltaic

KW - Solar Cell

KW - Hot-spots

KW - PV hot spot detection

KW - Fault Detection

KW - Statistical analysis

KW - thermal analysis

KW - Solar energy

KW - CDF modelling

KW - solar energy

KW - hot-spots

KW - performance ratio

KW - power loss

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