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.

has been cited by the following article:

• TITLE: Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

  AUTHORS: Kitwa Henock Bondo

  KEYWORDS: Online Fault Monitoring, OLTC, On-Load Tap Change, Voiceprint Detection

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.12 No.3, March 21, 2024

  ABSTRACT: 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.