### Abstract

The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp and 0.52 kWp GCPV systems installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lies out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function.

The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system. The maximum detection accuracy of the algorithm 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 logic system.

Original language | English |
---|---|

Pages (from-to) | 26-39 |

Number of pages | 14 |

Journal | Electric Power Systems Research |

Volume | 151 |

Early online date | 23 May 2017 |

DOIs | |

Publication status | Published - Oct 2017 |

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### Cite this

*Electric Power Systems Research*,

*151*, 26-39. https://doi.org/10.1016/j.epsr.2017.05.024

}

*Electric Power Systems Research*, vol. 151, pp. 26-39. https://doi.org/10.1016/j.epsr.2017.05.024

**Diagnostic Method for Photovoltaic Systems based on Six Layer Detection Algorithm.** / Dhimish, Mahmoud; Holmes, Violeta; Mehrdadi, Behrooz; Dales, Mark.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Diagnostic Method for Photovoltaic Systems based on Six Layer Detection Algorithm

AU - Dhimish, Mahmoud

AU - Holmes, Violeta

AU - Mehrdadi, Behrooz

AU - Dales, Mark

PY - 2017/10

Y1 - 2017/10

N2 - This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) plant. 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 (high and low limit) for the VR and PR ratios obtained using LabVIEW simulation tool.The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp and 0.52 kWp GCPV systems installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lies out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function.The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system. The maximum detection accuracy of the algorithm 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 logic system.

AB - This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) plant. 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 (high and low limit) for the VR and PR ratios obtained using LabVIEW simulation tool.The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp and 0.52 kWp GCPV systems installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lies out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function.The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system. The maximum detection accuracy of the algorithm 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 logic system.

KW - Photovoltaic system

KW - Photovoltaic faults

KW - Fault detection

KW - LabVIEW

KW - Fuzzy logic

UR - https://www.journals.elsevier.com/electric-power-systems-research

U2 - 10.1016/j.epsr.2017.05.024

DO - 10.1016/j.epsr.2017.05.024

M3 - Article

VL - 151

SP - 26

EP - 39

JO - Electric Power Systems Research

JF - Electric Power Systems Research

SN - 0378-7796

ER -