Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

Mahmoud Dhimish, Violeta Holmes, Bruce Mehrdadi, Mark Dales, Peter Mather

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool.

The high and low detection limits are compared with measured data taken from 1.1 kWp PV system installed at the University of Huddersfield, United Kingdom. Samples lie out of the detection limits are processed by a fuzzy logic classification system which consists of two inputs and one output membership function.

In this paper, PV faults corresponds to a short circuited PV module. The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27%. However, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy system.
LanguageEnglish
Pages276-290
Number of pages15
JournalEnergy
Volume140
Early online date25 Aug 2017
DOIs
Publication statusPublished - 1 Dec 2017

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Fault detection
Fuzzy logic
Electric potential
Fuzzy systems
Membership functions
Polynomials
Temperature

Cite this

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title = "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system",
abstract = "This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool.The high and low detection limits are compared with measured data taken from 1.1 kWp PV system installed at the University of Huddersfield, United Kingdom. Samples lie out of the detection limits are processed by a fuzzy logic classification system which consists of two inputs and one output membership function.In this paper, PV faults corresponds to a short circuited PV module. The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27{\%}. However, the fault detection accuracy is increased up to a minimum value of 98.8{\%} after considering the fuzzy system.",
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Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system. / Dhimish, Mahmoud; Holmes, Violeta; Mehrdadi, Bruce; Dales, Mark; Mather, Peter.

In: Energy, Vol. 140, 01.12.2017, p. 276-290.

Research output: Contribution to journalArticle

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T1 - Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

AU - Dhimish, Mahmoud

AU - Holmes, Violeta

AU - Mehrdadi, Bruce

AU - Dales, Mark

AU - Mather, Peter

PY - 2017/12/1

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N2 - This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool.The high and low detection limits are compared with measured data taken from 1.1 kWp PV system installed at the University of Huddersfield, United Kingdom. Samples lie out of the detection limits are processed by a fuzzy logic classification system which consists of two inputs and one output membership function.In this paper, PV faults corresponds to a short circuited PV module. The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27%. However, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy system.

AB - This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool.The high and low detection limits are compared with measured data taken from 1.1 kWp PV system installed at the University of Huddersfield, United Kingdom. Samples lie out of the detection limits are processed by a fuzzy logic classification system which consists of two inputs and one output membership function.In this paper, PV faults corresponds to a short circuited PV module. The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27%. However, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy system.

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