Expert System for Power Quality Disturbance Classifier
Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The co...
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| Format: | Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2007
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| Online Access: | http://shdl.mmu.edu.my/3038/ http://shdl.mmu.edu.my/3038/1/1061.pdf |
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| author | Ibne Reaz, Mamun Choong, Florence Chiao Mei Sulaiman, Mohd Shahiman Mohd Yasin, Faisal Kamada, Masaru |
| author_facet | Ibne Reaz, Mamun Choong, Florence Chiao Mei Sulaiman, Mohd Shahiman Mohd Yasin, Faisal Kamada, Masaru |
| author_sort | Ibne Reaz, Mamun |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The concept of discrete wavelet transform for feature extraction of power disturbance signal combined with artificial neural network and fuzzy logic incorporated as a powerful tool for detecting and classifying power quality problems. This paper employes a different type of univariate randomly optimized neural network combined with discrete wavelet transform and fuzzy logic to have a better power quality disturbance classification accuracy. The disturbances of interest include sag, swell, transient, fluctuation, and interruption. The system is modeled using VHSIC Hardware Description Language (VHDL), a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. This proposed method classifies, and achieves 98.19% classification accuracy for the application of this system on software-generated signals and utility sampled disturbance events. |
| first_indexed | 2025-11-14T18:09:06Z |
| format | Article |
| id | mmu-3038 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:09:06Z |
| publishDate | 2007 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-30382021-01-24T03:36:07Z http://shdl.mmu.edu.my/3038/ Expert System for Power Quality Disturbance Classifier Ibne Reaz, Mamun Choong, Florence Chiao Mei Sulaiman, Mohd Shahiman Mohd Yasin, Faisal Kamada, Masaru T Technology (General) QA75.5-76.95 Electronic computers. Computer science Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The concept of discrete wavelet transform for feature extraction of power disturbance signal combined with artificial neural network and fuzzy logic incorporated as a powerful tool for detecting and classifying power quality problems. This paper employes a different type of univariate randomly optimized neural network combined with discrete wavelet transform and fuzzy logic to have a better power quality disturbance classification accuracy. The disturbances of interest include sag, swell, transient, fluctuation, and interruption. The system is modeled using VHSIC Hardware Description Language (VHDL), a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. This proposed method classifies, and achieves 98.19% classification accuracy for the application of this system on software-generated signals and utility sampled disturbance events. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2007-07 Article NonPeerReviewed text en http://shdl.mmu.edu.my/3038/1/1061.pdf Ibne Reaz, Mamun and Choong, Florence Chiao Mei and Sulaiman, Mohd Shahiman and Mohd Yasin, Faisal and Kamada, Masaru (2007) Expert System for Power Quality Disturbance Classifier. IEEE Transactions on Power Delivery, 22 (3). pp. 1979-1988. ISSN 0885-8977 http://dx.doi.org/10.1109/TPWRD.2007.899774 doi:10.1109/TPWRD.2007.899774 doi:10.1109/TPWRD.2007.899774 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Ibne Reaz, Mamun Choong, Florence Chiao Mei Sulaiman, Mohd Shahiman Mohd Yasin, Faisal Kamada, Masaru Expert System for Power Quality Disturbance Classifier |
| title | Expert System for Power Quality Disturbance Classifier |
| title_full | Expert System for Power Quality Disturbance Classifier |
| title_fullStr | Expert System for Power Quality Disturbance Classifier |
| title_full_unstemmed | Expert System for Power Quality Disturbance Classifier |
| title_short | Expert System for Power Quality Disturbance Classifier |
| title_sort | expert system for power quality disturbance classifier |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/3038/ http://shdl.mmu.edu.my/3038/ http://shdl.mmu.edu.my/3038/ http://shdl.mmu.edu.my/3038/1/1061.pdf |