Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier
Identification and classification of voltage and current disturbances in power systems are important tasks in their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power qualit...
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| Format: | Article |
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TAYLOR & FRANCIS INC
2007
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| Online Access: | http://shdl.mmu.edu.my/3142/ |
| _version_ | 1848790245596725248 |
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| author | Reaz, M. B. I. Choong, F. Sulaiman, M. S. Mohd-Yasin, F. |
| author_facet | Reaz, M. B. I. Choong, F. Sulaiman, M. S. Mohd-Yasin, F. |
| author_sort | Reaz, M. B. I. |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Identification and classification of voltage and current disturbances in power systems are important tasks in their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power quality related events. This article focuses on the application of wavelet transform technique to extract features from power quality disturbance waveforms and their classification using a combination of artificial neural network and fuzzy logic. The disturbances of interest include sag, swell, transient, fluctuation and interruption waveform. The system is modelled using VHDL and synthesized to Mercury EP1M120F484C5 FPGA, tested and validated. Comparisons, verification and analysis on disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.19%. This novel and efficient method, and also implementation of the method in hardware based on FPGA technology, showed improved performance over existing approaches for power quality disturbance detection and classification. |
| first_indexed | 2025-11-14T18:09:33Z |
| format | Article |
| id | mmu-3142 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:09:33Z |
| publishDate | 2007 |
| publisher | TAYLOR & FRANCIS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-31422011-10-04T02:25:08Z http://shdl.mmu.edu.my/3142/ Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier Reaz, M. B. I. Choong, F. Sulaiman, M. S. Mohd-Yasin, F. 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 their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power quality related events. This article focuses on the application of wavelet transform technique to extract features from power quality disturbance waveforms and their classification using a combination of artificial neural network and fuzzy logic. The disturbances of interest include sag, swell, transient, fluctuation and interruption waveform. The system is modelled using VHDL and synthesized to Mercury EP1M120F484C5 FPGA, tested and validated. Comparisons, verification and analysis on disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.19%. This novel and efficient method, and also implementation of the method in hardware based on FPGA technology, showed improved performance over existing approaches for power quality disturbance detection and classification. TAYLOR & FRANCIS INC 2007-01 Article NonPeerReviewed Reaz, M. B. I. and Choong, F. and Sulaiman, M. S. and Mohd-Yasin, F. (2007) Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier. Electric Power Components and Systems, 35 (1). pp. 1-17. ISSN 1532-5008 http://dx.doi.org/10.1080/15325000600815431 doi:10.1080/15325000600815431 doi:10.1080/15325000600815431 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Reaz, M. B. I. Choong, F. Sulaiman, M. S. Mohd-Yasin, F. Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier |
| title | Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier |
| title_full | Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier |
| title_fullStr | Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier |
| title_full_unstemmed | Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier |
| title_short | Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier |
| title_sort | prototyping of wavelet transform, artificial neural network and fuzzy logic for power quality disturbance classifier |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/3142/ http://shdl.mmu.edu.my/3142/ http://shdl.mmu.edu.my/3142/ |