Multi‐layer photovoltaic fault detection algorithm. Issue 4 (1st December 2017)

• Record Type: Journal Article
• Authors: Dhimish, Mahmoud; Holmes, Violeta; Mehrdadi, Bruce; Dales, Mark
• Is Part Of: High voltage. Volume 2:Issue 4(2017)
• Page Start: 244
• Page End: 252
• Publication Date: 2017-12-01
• Subjects: fault diagnosis -- photovoltaic power systems -- power generation faults -- virtual instrumentation -- polynomials -- fuzzy logic -- power system simulation; multilayer photovoltaic fault detection algorithm -- theoretical curves -- grid‐connected photovoltaic system -- GCPV system -- voltage ratio -- power ratio -- virtual instrumentation -- LabVIEW software -- third‐order polynomial function -- PR ratios -- VR ratios -- real‐time long‐term data measurements -- University of Huddersfield -- United Kingdom -- fuzzy logic classification system -- output membership function -- maximum detection accuracy; High voltages -- Periodicals; 621.3191
• DOI: 10.1049/hve.2017.0044
• Languages: English
• ISSNs: 2397-7264

Abstract

Abstract : This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid‐connected photovoltaic (GCPV) system. For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software. Furthermore, a third‐order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios. The high and low detection limits are compared with real‐time long‐term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lie 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 accurately detects different faults occurring in the PV system. The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%; however, the fault DA is increased up to a minimum value of 98.8% after considering the fuzzy logic system.