Photovoltaic Hot-Spots Fault Detection Algorithm using Fuzzy Systems

Mahmoud Dhimish, Ghadeer Badran

Research output: Contribution to journalArticle

Abstract

Faults in photovoltaic (PV) modules, which might result in energy loss and reliability problems are often difficult to avoid, and certainty need to be detected. One of the major reliability problems affecting PV modules is hot-spotting, where a cell or group of cells heats up significantly compared to adjacent solar cells, hence decreasing the optimum power generated. In this article, we propose a fault detection of PV hot-spots based on the analysis of 2580 PV modules affected by different types of hot-spots, where these PV modules are operated under various environmental conditions, distributed across the UK. The fault detection model comprises a fuzzy inference system (FIS) using Mamdani-type fuzzy controller including three input parameters, namely, percentage of power loss (PPL), short circuit current (Isc), and open circuit voltage (Voc). In order to test the effectiveness of the proposed algorithm, extensive simulation and experimental-based tests have been carried out; while the average obtained accuracy is equal to 96.7%.
Original languageEnglish
Number of pages9
JournalIEEE Transactions on Device and Materials Reliability
Early online date1 Oct 2019
DOIs
Publication statusE-pub ahead of print - 1 Oct 2019

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Fuzzy systems
Fault detection
Fuzzy inference
Open circuit voltage
Short circuit currents
Energy dissipation
Solar cells
Controllers
Hot Temperature

Cite this

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title = "Photovoltaic Hot-Spots Fault Detection Algorithm using Fuzzy Systems",
abstract = "Faults in photovoltaic (PV) modules, which might result in energy loss and reliability problems are often difficult to avoid, and certainty need to be detected. One of the major reliability problems affecting PV modules is hot-spotting, where a cell or group of cells heats up significantly compared to adjacent solar cells, hence decreasing the optimum power generated. In this article, we propose a fault detection of PV hot-spots based on the analysis of 2580 PV modules affected by different types of hot-spots, where these PV modules are operated under various environmental conditions, distributed across the UK. The fault detection model comprises a fuzzy inference system (FIS) using Mamdani-type fuzzy controller including three input parameters, namely, percentage of power loss (PPL), short circuit current (Isc), and open circuit voltage (Voc). In order to test the effectiveness of the proposed algorithm, extensive simulation and experimental-based tests have been carried out; while the average obtained accuracy is equal to 96.7{\%}.",
keywords = "Photovoltaic (PV), Photovoltaic (PV) module performance, Hot-spots, Fuzzy classification, Fuzzy Logic, Fault Detection, Fault diagnosis, Renewable energy, I-V curve, Power loss",
author = "Mahmoud Dhimish and Ghadeer Badran",
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language = "English",
journal = "IEEE Transactions on Device and Materials Reliability",
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AU - Badran, Ghadeer

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N2 - Faults in photovoltaic (PV) modules, which might result in energy loss and reliability problems are often difficult to avoid, and certainty need to be detected. One of the major reliability problems affecting PV modules is hot-spotting, where a cell or group of cells heats up significantly compared to adjacent solar cells, hence decreasing the optimum power generated. In this article, we propose a fault detection of PV hot-spots based on the analysis of 2580 PV modules affected by different types of hot-spots, where these PV modules are operated under various environmental conditions, distributed across the UK. The fault detection model comprises a fuzzy inference system (FIS) using Mamdani-type fuzzy controller including three input parameters, namely, percentage of power loss (PPL), short circuit current (Isc), and open circuit voltage (Voc). In order to test the effectiveness of the proposed algorithm, extensive simulation and experimental-based tests have been carried out; while the average obtained accuracy is equal to 96.7%.

AB - Faults in photovoltaic (PV) modules, which might result in energy loss and reliability problems are often difficult to avoid, and certainty need to be detected. One of the major reliability problems affecting PV modules is hot-spotting, where a cell or group of cells heats up significantly compared to adjacent solar cells, hence decreasing the optimum power generated. In this article, we propose a fault detection of PV hot-spots based on the analysis of 2580 PV modules affected by different types of hot-spots, where these PV modules are operated under various environmental conditions, distributed across the UK. The fault detection model comprises a fuzzy inference system (FIS) using Mamdani-type fuzzy controller including three input parameters, namely, percentage of power loss (PPL), short circuit current (Isc), and open circuit voltage (Voc). In order to test the effectiveness of the proposed algorithm, extensive simulation and experimental-based tests have been carried out; while the average obtained accuracy is equal to 96.7%.

KW - Photovoltaic (PV)

KW - Photovoltaic (PV) module performance

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KW - Fault diagnosis

KW - Renewable energy

KW - I-V curve

KW - Power loss

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