This paper presents an application of Wavelet multiresolution analysis (MRA) for the identification and classification of high impedance faults. Daubechies eight (D-8) Wavelet transforms of three phase currents on transmission lines are used for the analysis. The peak absolute values, the mean of the peak absolute values and summation of the 3rd level output of MRA detail signals of current in ...

This paper presents a numerical study of the system unbalance and the fault impedance effect on faulted power system analysis. Two short circuit techniques, the symmetrical components and phase components algorithms are implemented and analyzed based on numerical simulations of the IEEE 13 bus test feeder. Test cases include voltage unbalance effect and fault impedance effect on during-fault vo...

Photovoltaic arc faults have led to a number of rooftop fires that have caused significant property damage and threatened the safety of building occupants. In response, Article 690.11 was added to the United States 2011 National Electrical Code, requiring new PV systems on or penetrating a building to include a listed arc fault protection device. Many proposed arc fault detectors utilize freque...

DC system protection is more challenging than that for AC due to the rapid rate of rise fault current and absence natural zero-crossing in systems. Superconducting limiter (SFCL) systems a promising technology reduce level current, also SFCLs have no resistance during normal operation. In this paper, behaviors an SFCL coil are investigated under both low impedance high conditions condition syst...

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection ML techniques learn patterns from data successfully HIFs. However, as these methods based on supervised learning, they fail reliably any scenario, fault or non-fault, not present the tra...

This paper presents a new method to classify and identify high impedance faults in radial distribution system. The proposed methodology uses extreme learning machine (ELM) as a classifier for identifying the high impedance arc-type faults. The network is learned by data from simulation of a simple radial system under different fault and system conditions. Magnitudes of third and fifth harmonic ...