Artificial neural network classification of power quality disturbances using time-frequency plane in industries

Proceedings - 1st International Conference on Emerging Trends in Engineering and Technology, ICETET 2008

Volumn , Issue , 2008, Pages 564-568

  Manke, Preeti R ^a   Tembhurne, S B ^b  

^a SHRI RAMDEOBABA COLLEGE OF ENGINEERING AND MANAGEMENT   (India)

^b NTPC LTD   (India)

Author keywords

Modified Fisher's Discriminant Ratio Kernel; Neural network; Power quality; Process industries

Indexed keywords

ARTIFICIAL NEURAL NETWORK; EMERGING TRENDS; FEED FORWARD; FISHER'S DISCRIMINANT; FUZZY CLASSIFICATION METHODS; IN-PROCESS; INTERNATIONAL CONFERENCES; MODIFIED FISHER'S DISCRIMINANT RATIO KERNEL; NEURAL NETWORK; NEW APPROACHES; POWER MONITORING; POWER QUALITY DISTURBANCES; POWER QUALITY MONITORING SYSTEMS; POWER SYSTEMS; PROCESS INDUSTRIES; PROTECTION SYSTEMS; RAPID GROWTH; TIME FREQUENCIES;

AUTOMATION; CLASSIFICATION (OF INFORMATION); ELECTRIC MEASURING INSTRUMENTS; ELECTRIC POWER SYSTEMS; FEATURE EXTRACTION; IMAGE CLASSIFICATION; METAL RECOVERY; NEURAL NETWORKS; SULFATE MINERALS; TECHNOLOGY;

POWER QUALITY;

EID: 52049120553     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICETET.2008.53     Document Type: Conference Paper

References (6)

1. B. Gillespie, L. E. Atlas, "Data-Driven Time- Frequency Classification Techniques Applied To Tool-Wear Monitoring," Proceedings of the 2000 IEEE ICASSP, 2000.
2. th International Conference On Harmonics and Quality of Power Proceedings, Vol. 1, pp. 77-82, 1998.
3. D. Mueller, M, McGranaghan, "Effects of voltage sags in process industry applications," http://www.pqnet.elcctrotek.corn/pGnet/main/ backgrnd/tutorial/sag/paper/paper.htm.
4. F. Hlawatsch, G. F. Boudreaux-Bartels, "Linear and quadratic time-frequency signal representations, " IEEE Signal Processing Magazine, Apr. 1992.
5. R. C. Dugan, Electrical Power Systems Quality, New York: McGraw-Hill, 1996.
6. S. Santoso, E. J. Powers, W. M. Grady, A. C. Parsons, "Power quality disturbance waveform recognition using wavelet-based neural classifier I. Theoretical foundation," IEEE Transactions on Power Delivery, Vol. 15, pp. 222-228, Jan. 2000.