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Application of two new ANN-based algorithms for arcing high impedance fault (HIF) detection in multigrounded medium-voltage (MV) distribution networks is presented in this paper. The paper provides an evaluation of two new structures of artificial neural networks (ANNs) that may be used for reliable HIF detection in multigrounded as well as isolated, compensated, and grounded via small resistance distribution grids. The results obtained by use of both neural nets are presented. The performance was tested using data obtained from staged HIFs in real MV network as well as from Electromagnetic Transients Program-Alternative Transients Program simulations. A small number of necessary neurons in developed ANNs, short measuring sliding data window, and easy interpretation of obtained output signals are the main advantages of the proposed approach. Satisfactory results of ANN performance were observed for all examined H1F cases in which the ground fault current was greater than 16 A. The selected ANNs of best performance show high reliability and immunity to transients resulting from switching operations in protected feeders and from capacitor bank switching. Index Terms--Artificial neural networks (ANNs), high impedance faults (HIFs), medium-voltage (MV) networks, transient analysis.

The application of the wavelet-based algorithm for arcing high-impedance fault detection medium-voltage (MV) distribution networks is presented in this paper. This paper describes continuation of research on HIF detection with particular reference to algorithm application in solidly grounded MV networks. The results obtained by use of the improved version of the algorithm are presented. The algorithm performance was tested using data obtained from staged HIFs in a real MV network as well as from EMTP-ATP simulations. Satisfactory results of the algorithm performance were observed for all examined HIF cases in which the ground fault current was greater than 3 A root mean square. The improved algorithm also proved to be more immune to transients generated during switching operations in protected feeders and to capacitor bank switching. Index Terms--High impedance faults, millivolt (mV) networks, transient analysis, wavelet transformation.

A new simple and effective algorithm of arcing fault detection in distribution networks with the application of a wavelet transform technique is presented in this paper. The protection algorithm developed observes the phase displacement between wavelet coefficients calculated for zero-sequence voltage and current signals at a chosen high-level frequency. The final decision in regards to feeder switching off (or alarm issuing) is met either with a deterministic logic scheme or with the use of a neural net trained especially for that purpose. The developed wavelet-based high-impedance fault (HIF) detector has been tested with Electromagnetic Transients Program-Alternative Transients Program (ATP)-generated signals, exhibiting better performance than traditionally used algorithms and methods. The protection method proposed may be used for HIF detection independent of the network neutral-point grounding mode. The scheme proved to be robust against transients generated during normal events such as feeder energizing and de-energizing as well as capacitor bank switching. Index Terms--High-impedance faults (HIFs), medium-voltage (MV) networks, protective relaying, transient analysis, wavelet transformation.