DWT-Based detection and transient power direction-based location of high-impedance faults due to leaning trees in unearthed MV networks

Electrical faults due to leaning trees are common in Nordic countries. This fault type has been studied in [11 and it was found that the initial transients in the electrical network due to the associated arc reignitions; are behavioral traits. In this paper, these features are extracted using the discrete wavelet transform (DWT) to localize this fault event. Wireless sensors are considered for processing the DWTs on a residual voltage of different measuring nodes that are distributed in the network. Therefore, the fault detection is confirmed by numerous DWT processors over a wide area of the network. The detection security is also enhanced because the DWT responded to a periodicity of the initial transients. The term for locating the faulty section is based on the polarity of a specific frequency bandpower computed by multiplying the DWT detail coefficient of the residual current and voltage at each measuring node. The fault due to a leaning tree occurring at different locations in an unearthed 20-kV network is simulated by the Alternate Transients Program/Electromagnetic Transients Program and the arc model is implemented using the universal arc representation. Test cases provide evidence of the efficacy of the proposed technique.