TY - GEN
T1 - Identification of Short Duration Voltage Variations Based on Short Time Fourier Transform and Artificial Neural Network
AU - Anggriawan, Dimas Okky
AU - Wahjono, Endro
AU - Sudiharto, Indhana
AU - Firdaus, Aji Akbar
AU - Novita Nurmala Putri, Dianing
AU - Budikarso, Anang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - This paper presents the proposed algorithms for identification of short duration voltage variations (SDVV) as voltage sag dan voltage swell. The proposed algorithms are short time fourier transform (STFT) and artificial neural network (ANN). STFT is used to signal analysis of SDVV. However, SDVV have characteristics of non-stationary signal, which it is not can be detected by fast fourier transform (FFT). Moreover, ANN is used to identification of SDVV, which it identifies five types of SDVV such as normal signal, voltage sag, voltage swell, voltage sag combined harmonic and voltage swell combined harmonic. Output STFT is used to ANN for identification. The simulation is conducted by STFT comparing of FFT. Whereas, to evaluate of ANN with variation of neurons. The simulation result show that STFT more accurate compared by FFT to detection of SDVV. Moreover, ANN has good accuracy for SDVV types identification, which ANN with 10 x 10 neurons in hidden layer has accuracy of 100 %.
AB - This paper presents the proposed algorithms for identification of short duration voltage variations (SDVV) as voltage sag dan voltage swell. The proposed algorithms are short time fourier transform (STFT) and artificial neural network (ANN). STFT is used to signal analysis of SDVV. However, SDVV have characteristics of non-stationary signal, which it is not can be detected by fast fourier transform (FFT). Moreover, ANN is used to identification of SDVV, which it identifies five types of SDVV such as normal signal, voltage sag, voltage swell, voltage sag combined harmonic and voltage swell combined harmonic. Output STFT is used to ANN for identification. The simulation is conducted by STFT comparing of FFT. Whereas, to evaluate of ANN with variation of neurons. The simulation result show that STFT more accurate compared by FFT to detection of SDVV. Moreover, ANN has good accuracy for SDVV types identification, which ANN with 10 x 10 neurons in hidden layer has accuracy of 100 %.
KW - Short time fourier transform
KW - artificial neural network
KW - identification
KW - power quality disturbances
KW - short duration voltage variations
UR - http://www.scopus.com/inward/record.url?scp=85096778079&partnerID=8YFLogxK
U2 - 10.1109/IES50839.2020.9231815
DO - 10.1109/IES50839.2020.9231815
M3 - Conference contribution
AN - SCOPUS:85096778079
T3 - IES 2020 - International Electronics Symposium: The Role of Autonomous and Intelligent Systems for Human Life and Comfort
SP - 43
EP - 47
BT - IES 2020 - International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Hermawan, Hendhi
A2 - Mu'arifin, Mu'arifin
A2 - Muliawati, Tri Hadiah
A2 - Putra, Putu Agus Mahadi
A2 - Gamar, Farida
A2 - Ridwan, Mohamad
A2 - Kusuma N, Artiarini
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Electronics Symposium, IES 2020
Y2 - 29 September 2020 through 30 September 2020
ER -