DWT-based extraction of residual currents throughout unearthed MV networks for detecting high-impedance faults due to leaning trees

SUMMARY Modelling of a high-impedance arcing fault due to a leaning tree in medium voltage (MV) networks was experimentally verified and the network transients due to this fault were also investigated. Even though the tree had a very high resistance value, the initial transients were periodically caused by the arc reignitions after each zero-crossing. In this paper, these features are extracted from residual currents using discrete wavelet transform (DWT) to localise this fault event. The DWT performance at different measuring nodes throughout an unearthed 20 kV network can be gathered at the base station using wireless sensors concept. So, the DWT is evaluated for a wide area of the network and the fault detection is confirmed by numerous DWT extractors. Due to the periodicity of arc reignitions, the initial transients are localised not only at fault starting instant but also during the fault period that will enhance the detection security. The term of locating the faulty section is determined based on ratios of the residual current amplitudes. The fault cases are simulated by ATP/EMTP and the arc model is implemented using the universal arc representation.