Novel Photovoltaic Hot-spotting Fault Detection Algorithm

Mahmoud Dhimish; Peter Mather; Violeta Holmes

doi:10.1109/TDMR.2019.2910196

Novel Photovoltaic Hot-spotting Fault Detection Algorithm

Mahmoud Dhimish, Peter Mather, Violeta Holmes

Research output: Contribution to journal › Article › peer-review

28 Citations (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 language	English
Article number	8685128
Pages (from-to)	378-386
Number of pages	9
Journal	IEEE Transactions on Device and Materials Reliability
Volume	19
Issue number	2
Early online date	11 Apr 2019
DOIs	https://doi.org/10.1109/TDMR.2019.2910196
Publication status	Published - 5 Jun 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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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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T1 - Novel Photovoltaic Hot-spotting Fault Detection Algorithm

AU - Dhimish, Mahmoud

AU - Mather, Peter

AU - Holmes, Violeta

PY - 2019/6/5

Y1 - 2019/6/5

N2 - 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%.

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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DO - 10.1109/TDMR.2019.2910196

M3 - Article

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JO - IEEE Transactions on Device and Materials Reliability

JF - IEEE Transactions on Device and Materials Reliability

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ER -

Novel Photovoltaic Hot-spotting Fault Detection Algorithm

Abstract

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