TR2013-024

Transient Disturbance Detection for Power Systems with a General Likelihood Ratio Test


    •  Song, J.X., Sahinoglu, Z., Guo, J., "Transient Disturbance Detection for Power Systems with a General Likelihood Ratio Test", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
      BibTeX TR2013-024 PDF
      • @inproceedings{Song2013may,
      • author = {Song, J.X. and Sahinoglu, Z. and Guo, J.},
      • title = {Transient Disturbance Detection for Power Systems with a General Likelihood Ratio Test},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2013,
      • month = may,
      • url = {https://www.merl.com/publications/TR2013-024}
      • }
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  • Research Area:

    Signal Processing

Abstract:

A voltage/current transient typically caused by islanding and switching operations is treated as an adverse phenomenon that degrades power quality, and it may cause damage to electrical equipment. Therefore, a reliable system should effectively detect and monitor a transient disturbance. In this paper, the transient detection problem is formulated as a binary hypothesis test: normal signal (null) vs. transient (alternative). The sampled data is described by a sinusoid under the null hypothesis, while a sum of damped sinusoids is utilized to model the alternative one. As no prior knowledge is imposed on complex amplitudes, frequencies, or damping factors in signal modeling, the general likelihood ratio test (GLRT) is employed to fulfill the task. To reduce computational complexity, the maximum likelihood estimator is replaced by ESPRIT for parameter estimation. Probability of detection of 0.98 is achieved at a SNR of 27dB and probability of false alarm of 0.0005.

 

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      Date: May 26, 2013
      Where: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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      • The papers "Stereo-based Feature Enhancement Using Dictionary Learning" by Watanabe, S. and Hershey, J.R., "Effectiveness of Discriminative Training and Feature Transformation for Reverberated and Noisy Speech" by Tachioka, Y., Watanabe, S. and Hershey, J.R., "Non-negative Dynamical System with Application to Speech and Audio" by Fevotte, C., Le Roux, J. and Hershey, J.R., "Source Localization in Reverberant Environments using Sparse Optimization" by Le Roux, J., Boufounos, P.T., Kang, K. and Hershey, J.R., "A Keypoint Descriptor for Alignment-Free Fingerprint Matching" by Garg, R. and Rane, S., "Transient Disturbance Detection for Power Systems with a General Likelihood Ratio Test" by Song, JX., Sahinoglu, Z. and Guo, J., "Disparity Estimation of Misaligned Images in a Scanline Optimization Framework" by Rzeszutek, R., Tian, D. and Vetro, A., "Screen Content Coding for HEVC Using Edge Modes" by Hu, S., Cohen, R.A., Vetro, A. and Kuo, C.C.J. and "Random Steerable Arrays for Synthetic Aperture Imaging" by Liu, D. and Boufounos, P.T. were presented at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
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