Penetration of DWT & ANFIS to Power Transmission Disturbances

Sandy Ahmad; Azriyenni  Azhari Zakri; Muchamad  Oktaviandri; Wahri  Sunanda; Aris  Suryadi

doi:10.31258/ijeepse.6.1.105-110

Sandy Ahmad Universitas Riau, Pekanbaru, Indonesia
Azriyenni Azhari Zakri Universitas Riau, Pekanbaru, Indonesia
Muchamad Oktaviandri Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia
Wahri Sunanda Universitas Bangka Belitung, Bangka, Indonesia
Aris Suryadi Politeknik Enjinering Indorama Purwakarta, Purwakarta, Indonesia

DOI: https://doi.org/10.31258/ijeepse.6.1.105-110

Abstract viewed: 335 times

pdf downloaded: 277 times

Keywords: ANFIS, DWT, disturbance, hybrid, occur

Abstract

This study proposes a hybrid method to classify and estimate the location of short circuit disturbance on power transmission lines. The hybrid method uses Discrete Wavelet Transform (DWT) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The transmission system is implemented in a real system, in which the electric power transmission system on the KP bus to the GS bus is with a length of 64 Km. The DWT is used to process information from each phase voltage and current transient signal as well as the zero-sequence current for one cycle after the disturbance has started. The ANFIS classification is designed to detect disturbance on each phase and ground in determining the type of short circuit disturbance. ANFIS estimation is used to measure the location of disturbance that occur on the transmission line. The training and testing data are generated by simulating the types of short circuit disturbance using software with variations in disturbance location and fault resistance. The result is that the disturbance classification is with 100% accuracy and the estimated disturbance location is with the lowest error of 0.0006% and the highest error is 0.03%.

References

H. Saber, “Accurate disturbance classifier and locator for EHV transmission lines based on artificial neural networks,” Math Probl Eng, 2014.

A. Saber, A. Emam, and R. Amer, “Discrete wavelet transform and support vector machine-based parallel transmission line faults classification,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 11, no. 1, pp. 43–48, 2016.

W. Li et al., “Fault Detection in Distribution Lines Using Artificial Neural Networks,” IEEE Transactions on Power Delivery, vol. 5, no. 5, 2017.

M. Misiti, Y. Misiti, G. Oppenheim, and J. M. Poggi, Wavelet toolbox: for use with MATLAB: user’s guide: version 3, III. The MathWorks, Inc., 2006.

M. Rucka and K. Wilde, “Application of continuous wavelet transform in vibration-based damage detection method for beams and plates,” J Sound Vib, vol. 297, pp. 536–550, 2006.

N. G. S. Jamali, “A new method for arcing fault location using discrete wavelet transform and wavelet networks,” European Transactions on Electrical Power, vol. 22, no. 5, pp. 601–615, 2012.

M. R. Mosavi and A. Tabatabaei, “Wavelet and neural network-based fault location in power systems using statistical analysis of traveling wave,” Arab J Sci Eng, vol. 8, pp. 1–8, 2014.

P. Nickolas, Wavelets: A Student Guide. 2017.

A. Teolis, Computational Signal Processing with Wavelets. Boston, 2017.

Amer, “Discrete wavelet transform and support vector machine-based parallel transmission line faults classification,” IEEJ Trans, vol. 11, pp. 43–48, 2016.

A. A. Zakri, S. Darmawan, S. Ahmad, M. W. Mustafa, and J. Usman, “Qualified two-hybrid techniques by DWT output to predict fault location,” Indonesian Journal of Electrical Engineering and Informatics, vol. 8, no. 4, pp. 806–817, Dec. 2020.

Azriyenni; M. Wazir Mustafa, “Application of ANFIS for Distance Relay Protection in Transmission Line,”,” International Journal of Electrical and Computer Engineering (IJECE), vol. 5, no. 6, pp. 1311–1318, 2015.

A. Azriyenni and M. E. Dame, “Pemodelan Struktur Teknik Cerdas Untuk Sistem Proteksi Daya Listrik,” SINERGI, vol. 21, no. 1, p. 31, Feb. 2017.

M. S. Abdel Aziz, M. A. Moustafa Hassan, and E. A. Zahab, “Applications of ANFIS in high impedance faults detection and classification in distribution networks,” SDEMPED 2011 - 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, pp. 612–619, 2011.

H. C. Chin, “Fault section diagnosis of power system using fuzzy logic,” IEEE Transactions on Power Systems, vol. 18, no. 1, pp. 245–250, 2003.

M. W. Azriyenni, Mustafa, “Performance Neuro-Fuzzy for Power System Fault Location,” IJET UK, vol. 3, no. 4, pp. 497–501, 2013.

A. A. Zakri, “Fault Diagnosis for Transmission Lines Systems Using ANFIS Techniques,” 2018.

T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE) Arguments against avoiding RMSE in the literature,” Geosci Model Dev, vol. 7, no. 3, pp. 1247–1250, Jun. 2014.