Power quality disturbance detection using artificial intelligence: A hardware approach

F. Choong, Md. Mamun Ibne Reaz, F. Mohd-Yasin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

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%.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005
Volume2005
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005 - Denver, CO
Duration: 4 Apr 20058 Apr 2005

Other

Other19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005
CityDenver, CO
Period4/4/058/4/05

Fingerprint

Power quality
Artificial intelligence
Hardware
Electric power system measurement
Neural networks
Computer hardware description languages
Discrete wavelet transforms
Intelligent systems
Expert systems
Wavelet transforms
Fuzzy logic
Field programmable gate arrays (FPGA)
Testing
Electric potential

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Choong, F., Ibne Reaz, M. M., & Mohd-Yasin, F. (2005). Power quality disturbance detection using artificial intelligence: A hardware approach. In Proceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005 (Vol. 2005). [1419993] https://doi.org/10.1109/IPDPS.2005.348

Power quality disturbance detection using artificial intelligence : A hardware approach. / Choong, F.; Ibne Reaz, Md. Mamun; Mohd-Yasin, F.

Proceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005. Vol. 2005 2005. 1419993.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Choong, F, Ibne Reaz, MM & Mohd-Yasin, F 2005, Power quality disturbance detection using artificial intelligence: A hardware approach. in Proceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005. vol. 2005, 1419993, 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005, Denver, CO, 4/4/05. https://doi.org/10.1109/IPDPS.2005.348
Choong F, Ibne Reaz MM, Mohd-Yasin F. Power quality disturbance detection using artificial intelligence: A hardware approach. In Proceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005. Vol. 2005. 2005. 1419993 https://doi.org/10.1109/IPDPS.2005.348
Choong, F. ; Ibne Reaz, Md. Mamun ; Mohd-Yasin, F. / Power quality disturbance detection using artificial intelligence : A hardware approach. Proceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005. Vol. 2005 2005.
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