The development of modern protection functions is a challenge in the emerging environment of smart grids because the current protection system technology still has several limitations, such as the reliable high impedance fault (HIF) detection in multi-grounded distribution networks, which poses a danger to the public when the protection system fails. This paper presents the wavelet coefficient energy with border distortions of a onecycle sliding window designed for the real-time detection of transients induced by HIFs. By using the border distortions, the proposed wavelet-based methodology presents a reliable detection of transients generated by HIFs with no time delay and energy peaks scarcely affected by the choice of the mother wavelet. The signature of different HIFs are presented in both time and wavelet domains. The performance of the proposed wavelet-based method was assessed with compact and long mother wavelets by using data from staged HIFs on an actual energized power system taking into account different fault surfaces as well as simulated HIFs. The proposed method presented a more reliable and accurate performance than other evaluated wavelet-based algorithms.Index Terms-High impedance faults, power system protection, wavelet transform.
0093-9994 (c)
A sampling frequency evaluation used in digital fault recorders for fault locations was implemented. A chained structure of artificial neural networks (ANN) was adopted to locate the faults. The ATP (Alternative Transient Program) software was used in the building of the database for training, testing and validation of the ANN, with different sampling frequencies. The input to the ANN are phase quantities and zero sequence voltage and current waveform data. The fault conditions were simulated for a 230 kV transmission line. The database used was generated automatically from a standard format file, and run in batch mode. For the fault location, the transmission line was divided into 8 zones. Previous to location, classification of the fault type is performed by training the ANN with the full line data. For the location, eight ANN were trained for each fault type, each one with the data of each zone.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.