NDLI: Power quality disturbance detection using artificial intelligence: a hardware approach

Content Provider	IEEE Xplore Digital Library
Author	Choong, F. Reaz, M.B.I. Mohd-Yasin, F.
Copyright Year	2005
Description	Author affiliation: Multimedia Univ., Cyberjaya, Malaysia (Choong, F.)
Abstract	Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. New intelligent system technologies using wavelet transform, expert systems and artificial neural networks provide some unique advantages regarding fault analysis. This paper presents new approach aimed at automating the analysis of power quality disturbances including sag, swell, transient, fluctuation, interruption and normal waveform. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a powerful combination of neural network and fuzzy logic. The system is modelled using VHDL followed by extensive testing and simulation to verify the correct functionality of the system. Then, the design is synthesized to APEX EP20K200EBC652-1X FPGA, tested and validated. Comparisons, verification and analysis made from the results obtained from the application of this system on software-generated and utility sampled disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.17%.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Parallel Process
File Size	134865
File Format	PDF
ISBN	0769523129
DOI	10.1109/IPDPS.2005.348
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power quality Artificial intelligence Hardware Power system protection Discrete wavelet transforms Power system analysis computing Power system faults Voltage Monitoring Intelligent systems
Content Type	Text
Resource Type	Article

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