@inproceedings{01ebe7ccb7314a00b8a68595fc21e322,
title = "Neuro wavelet algortihm for detecting high impedance faults in extra high voltage transmission systems",
abstract = "High impedance faults are not easy to be measured and detected by convetional relay protection. This paper proposed simualtions studies for detection high impedance fault in extra high voltage transmission line (EVT). The fault simulations based on simplified 2 diodes model. Current signal from the measurement is processed using discrete wavelet transform type haar wavelet to obtain coefficient detail. The output of discrete wavelet transform will be used for pattern recognition based on an backpropagation neural networks algorithm. The fault is modified to distribution system for EVT. The Characteristics of the proposed scheme are analyzed by comprehensive studies and the result clearly explain that it can accurately detect high impedance fault in the EVT with varies condition.",
keywords = "haar wavelet transform, high impedance faults, pattern recognition",
author = "Isa Hafidz and Nofi, \{P. Elyza\} and Anggriawan, \{DImas Okky\} and Ardyono Priyadi and Pumomo, \{Mauridhi Hery\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd International Conference on Sustainable and Renewable Energy Engineering, ICSREE 2017 ; Conference date: 10-05-2017 Through 12-05-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICSREE.2017.7951519",
language = "English",
series = "2017 International Conference on Sustainable and Renewable Energy Engineering, ICSREE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "97--100",
booktitle = "2017 International Conference on Sustainable and Renewable Energy Engineering, ICSREE 2017",
address = "United States",
}