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

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.
- Konkuk University Korea (Republic of)
- Konkuk University Korea (Republic of)
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).15 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.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
