TY - GEN
T1 - Detection of Series AC Arc Fault Based on Continuous Wavelet Transform and Artificial Neural Network under Voltage Variation Disturbances
AU - Anggriawan, Dimas Okky
AU - Priyadi, Ardyono
AU - Pujiantara, Margo
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Series arc fault in electrical power system can cause electrical fires. In distribution system, power quality disturbances include voltage variation disturbances often occurs. Voltage variation disturbances can lead to non-stationary waveform, which affect accuracy arc fault detection. To overcome this problem, this paper proposes combination methods of continuous wavelet transform and artificial neural network for detection. Continuous wavelet transform is superior to analyze of non-stationary waveform from series arc fault under voltage variation disturbances. Continuous wavelet transform recognizes series arc fault under voltage variation disturbances by transform of signal to time-frequency domain. Artificial neural network using type of feed forward neural network with Levenberg Marquardt Algorithm for series arc fault identification by data obtained from continuous wavelet transform. Data is trained and tested by artificial neural network. Several waveform model of series arc fault under voltage variation disturbances are selected to tested include normal system, series arc fault under normal condition, series arc fault under voltage sag condition and series arc fault under voltage swell condition. The result show that the algorithm of continuous wavelet transform and artificial neural network have good accuracy for series arc fault detection under normal system, series arc fault, voltage sag condition and voltage swell condition with the accuracy of 99.69 %, 99.2%, 99.97% and 99.9%, respectively.
AB - Series arc fault in electrical power system can cause electrical fires. In distribution system, power quality disturbances include voltage variation disturbances often occurs. Voltage variation disturbances can lead to non-stationary waveform, which affect accuracy arc fault detection. To overcome this problem, this paper proposes combination methods of continuous wavelet transform and artificial neural network for detection. Continuous wavelet transform is superior to analyze of non-stationary waveform from series arc fault under voltage variation disturbances. Continuous wavelet transform recognizes series arc fault under voltage variation disturbances by transform of signal to time-frequency domain. Artificial neural network using type of feed forward neural network with Levenberg Marquardt Algorithm for series arc fault identification by data obtained from continuous wavelet transform. Data is trained and tested by artificial neural network. Several waveform model of series arc fault under voltage variation disturbances are selected to tested include normal system, series arc fault under normal condition, series arc fault under voltage sag condition and series arc fault under voltage swell condition. The result show that the algorithm of continuous wavelet transform and artificial neural network have good accuracy for series arc fault detection under normal system, series arc fault, voltage sag condition and voltage swell condition with the accuracy of 99.69 %, 99.2%, 99.97% and 99.9%, respectively.
KW - Series arc fault
KW - artificial neural network
KW - continuous wavelet transform
KW - detection
KW - voltage variation disturbances
UR - http://www.scopus.com/inward/record.url?scp=85205030529&partnerID=8YFLogxK
U2 - 10.1109/IES63037.2024.10665869
DO - 10.1109/IES63037.2024.10665869
M3 - Conference contribution
AN - SCOPUS:85205030529
T3 - 2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
SP - 49
EP - 53
BT - 2024 International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Ramadhani, Afifah Dwi
A2 - Prayogi, Yanuar Risah
A2 - Putra, Putu Agus Mahadi
A2 - Rahmawati, Weny Mistarika
A2 - Rusli, Muhammad Rizani
A2 - Humaira, Fitrah Maharani
A2 - Nadziroh, Faridatun
A2 - Sa'adah, Nihayatus
A2 - Muna, Nailul
A2 - Rizki, Aris Bahari
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Electronics Symposium, IES 2024
Y2 - 6 August 2024 through 8 August 2024
ER -