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A Novel Fault Location Methodology for Smart Distribution Networks

Power distribution networks (PDNs) has played a crucial role in expediting transition towards cleaner and better distributed energy sources. Nowadays, more and more distributed generations (DGs) are used in PDNs which complicates the automatic fault location. This article presents an accurate impedance-based method to determine the fault location for smart PDN in the presence of DGs. In addition, phase domain equations of distributed line parameters are used to enhance the accuracy of fault location. Two types of networks are considered. The first type of network is assumed to be fully observable with $\mu PMU$ and in the second type there are only a few $\mu PMU\text{s}$ with data loggers on the rest nodes. Load impedances of all nodes are estimated using pre-fault recorded information by present $\mu PMU\text{s}$ and data loggers. The proposed algorithm might suggest several points as possible fault locations for a PDN. To find out the actual location of fault same fault type is simulated for all suggested points. A matching value which is mathematically defined in the article, is calculated using recorded and simulated voltage to determine the actual fault point among all the suggested candidates. The accuracy of suggested method is analyzed against various conditions.
- University of Southern Denmark Denmark
- Persian Gulf University Iran (Islamic Republic of)
- Persian Gulf University Iran (Islamic Republic of)
distributed generation, section estimation, Smart distribution network, load estimation
distributed generation, section estimation, Smart distribution network, load estimation
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).74 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 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
