Mechanical Fault Diagnostics of Onload Tap Changer Within Power Transformers Based on Hidden Markov Model

Online monitoring of the mechanical performance of onload tap changers (OLTCs) within high-voltage (HV) power transformers is of utmost significance for a safe, stable, and reliable operation of the power systems. This paper investigated a novel strategy based on a Hidden Markov Model (HMM) for mechanical fault diagnosis of OLTCs. With partition, normalization, and vector quantization of the power spectral density of the obtained vibration signals, a feature vector extraction methodology was presented for the discrete power spectrums which, to the farthest extent, could retain the unique features and difference of various mechanical condition modes, and well meet the requirement for the HMM exemplar training. With the sampled data series from experimental study and onsite measurements, a trained HMM norm modes library was established for different mechanical conditions of the OLTC. A large amount of function verifications demonstrated that the proposed HMM-based mechanical fault diagnosis scheme for OLTC is feasible and effective, with outstanding behavior for fault classification plus an identification rate of 95% in accuracy. An Internet-based program with preferable expandability has also been developed for practical applications of the proposed strategy in HV substations.

[1]  Yongyong He,et al.  Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery , 2005 .

[2]  Lowell LeRoy Winger.  Linearly constrained generalized Lloyd algorithm for reduced codebook vector quantization , 2001, IEEE Trans. Signal Process..

[3]  Pengju Kang,et al.  Condition monitoring of power transformer on-load tap-changers. Part 2: Detection of ageing from vibration signatures , 2001 .

[4]  Kenneth A. Loparo,et al.  A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[5]  V. Makis,et al.  Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models , 2007 .

[6]  Les E. Atlas,et al.  Hidden Markov models for monitoring machining tool-wear , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[7]  D. Birtwhistle,et al.  Condition Assessment of Power Transformer on-Load Tap-Changers Using Wavelet Analysis and Self-Organizing Map: Field Evaluation , 2002, IEEE Power Engineering Review.

[8]  Qian Jia-li.  ON-LINE DETECTION OF OLTC MECHANICAL PERFORMANCE , 2003 .

[9]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[10]  Pengju Kang,et al.  Condition Monitoring of Power Transformer On-Load Tap-Changers. Part 1: Automatic Condition Diagnostics , 2001 .