The
continuous operation of On-Load Tap-Changers (OLTC) is essential for
maintaining stable voltage levels in power transmission and distribution
systems. Timely fault detection in OLTC is essential for preventing major
failures and ensuring the reliability of the electrical grid. This research
paper proposes an innovative approach that combines voiceprint detection using
MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced
signal processing techniques and machine learning algorithms in MATLAB, the
proposed method accurately detects faults in OLTC, providing real-time
monitoring and proactive maintenance strategies.
References
[1]
Wang, L. and Liu, X.D. (2009) Manufacturing Technology. IEEE, 1, 1-4.
[2]
Aziz, N., Mughees, M., Hwang, J. and Tchuen, A.M. (2016) Online Tap Changer Condition Monitoring Using Vibration Analysis. IEEE Transactions on Power Delivery, 31, 469-477.
[3]
Li, Y., Jiang, X., Wang, L. and Liu, Y. (2019) Optimized Voiceprint Recognition for Fault Diagnosis of On-Load Tap-Changer. Proceedings of the CSEE, 39, 4735-4743.
[4]
Ampbell, J.P. and Sturim, D.E. (2010) Speaker Verification with the Fusion of Multiple Systems. IEEE Signal Processing Magazine, 27, 94-112.
[5]
Reynolds, D.A. (2002) An Overview of Automatic Speaker Recognition Technology. 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, 13-17 May 2002.
[6]
Reynolds, D.A. and Rose, R.C. (1995) Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models. IEEE Transactions on Speech and Audio Processing, 3, 72-83. https://doi.org/10.1109/89.365379
[7]
Jian, M. and Yongmei, L. (2015) An Embedded Voiceprint Recognition System Based on GMM. 2015 10th International Conference on Computer Science & Education (ICCSE), Cambridge, 22-24 July 2015, 38-41. https://doi.org/10.1109/ICCSE.2015.7250214
[8]
Wang, J., Liu, Y., Zhou, X., Chen, Y. and Zhang, W. (2018) Online Fault Diagnosis of On-Load Tap-Changer Based on Voiceprint Recognition. Electric Power Automation Equipment, 38, 14-19.
[9]
Li, Y., Wan, Y., Chen, H., Wu, X. and Huang, X. (2019) Tap Changer Condition Monitoring Based on Contact Resistance and Current Waveform Analysis. Energies, 12, 685.
[10]
Zhang, S., Teng, B., Ke, J., Zhang, Z. and Yu, T. (2020) Fault Classification of On-Load Tap-Changer Based on Voiceprint Recognition. CSEE Journal of Power and Energy Systems, 6, 122-129.
[11]
Xu, J., Zhou, C., Huang, C. and Tan, W. (2021) Automatic Fault Classification of On-Load Tap-Changer Based on Voiceprint Recognition Using Convolutional Neural Network. Power System Technology, 45, 1389-1396.
[12]
Paggi, M., Carrara, S. and Montagnari, F. (2018) Tap Changer Condition Assessment by Means of Temperature Monitoring. IEEE Transactions on Power Delivery, 33, 2899-2906.
[13]
Goo, E.C., Kim, Y.K. and Kim, C. (2020) Tap Changer Diagnostic Method for Incipient Faults Using Acoustic Emission. International Journal of Electrical Power & Energy Systems, 120, Article 106057.
[14]
Ali, M.A., Babar, M.M., Afaq, F. and Malik, N.H. (2021) A Machine Learning-Based Tap Changer Fault Detection Approach Using Electrical Measurements. IEEE Access, 9, 33309-33319.
[15]
Lin, W., Yang, L. and Xu, B. (2006) Based on the Modified MFCC Parameters of Chinese Whispered Speech Recognition. Journal of Nanjing University, 38, 364-368.
