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IEEE Journal of Photovoltaics
Article . 2021 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Fault Detection for Photovoltaic Systems Using Multivariate Analysis With Electrical and Environmental Variables

Authors: Gyu Gwang Kim; Wonbin Lee; Byeong Gwan Bhang; Jin Ho Choi; Hyung-Keun Ahn;

Fault Detection for Photovoltaic Systems Using Multivariate Analysis With Electrical and Environmental Variables

Abstract

Fault detection and repair of the components of photovoltaic (PV) systems are essential to avoid economic losses and facility accidents, thereby ensuring reliable and safe systems. This article presents a method to detect faults in a PV system based on power ratio (PR), voltage ratio (VR), and current ratio (IR). The lower control limit (LCL) and upper control limit (UCL) of each ratio were defined using the data of a test site system under normal operating conditions. If PR exceeded the set range, the algorithm considered a fault. Subsequently, PR and IR were examined via the algorithm to diagnose faults in the system as series, parallel, or total faults. The results showed that PR exceeded the designated range between LCL (0.93) and UCL (1.02) by dropping to 0.91–0.68, 0.88–0.62, and 0.66–0.33 for series, total, and parallel faults, respectively. Moreover, VR exceeded the LCL (0.99) and UCL (1.01) by 0.95–0.69 and 0.91–0.62 for series and total faults, respectively, but not under parallel faults condition. IR did not change in series and total faults but exceeded the range of LCL (0.93) and UCL (1.05) by dropping to 0.66–0.33. Thus, faults in PV systems can be detected and diagnosed by analyzing quantitative output values.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Top 10%
Average
Top 10%
hybrid