Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks
A novel approach for detection and classification of power quality (PQ) disturbances is proposed. The distorted waveforms (PQ events) are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with hig...
| Main Authors: | , , |
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| Format: | Journal Article |
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Institution of Engineering and Technology
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/17635 |
| _version_ | 1848749517424295936 |
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| author | Masoum, Mohammad Jamali, S. Ghaffarzadeh, N. |
| author_facet | Masoum, Mohammad Jamali, S. Ghaffarzadeh, N. |
| author_sort | Masoum, Mohammad |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A novel approach for detection and classification of power quality (PQ) disturbances is proposed. The distorted waveforms (PQ events) are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio. The DWT is also used to decompose the signal of PQ events and to extract its useful information. Proper feature vectors are selected and applied in training the wavelet network classifier. The effectiveness of the proposed method is tested using a wide spectrum of PQ disturbances including dc offset, harmonics, flicker, interrupt, sag, swell, notching, transient and combinations of these events. Comparison of test results with those generated by other existing methods shows enhanced performance with a classification accuracy of 98.18%. The main contribution of the paper is an accurate (because of proper selection of feature vectors), fast (e.g. a new de-noising approach with proposed identification criterion) and robust (at different signal-to-noise ratios) wavelet network-based algorithm (as compared to the conventional wavelet-based algorithms) for detection/classification of individual, as well as combined PQ disturbances. |
| first_indexed | 2025-11-14T07:22:12Z |
| format | Journal Article |
| id | curtin-20.500.11937-17635 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:22:12Z |
| publishDate | 2010 |
| publisher | Institution of Engineering and Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-176352017-09-13T15:42:22Z Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks Masoum, Mohammad Jamali, S. Ghaffarzadeh, N. A novel approach for detection and classification of power quality (PQ) disturbances is proposed. The distorted waveforms (PQ events) are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio. The DWT is also used to decompose the signal of PQ events and to extract its useful information. Proper feature vectors are selected and applied in training the wavelet network classifier. The effectiveness of the proposed method is tested using a wide spectrum of PQ disturbances including dc offset, harmonics, flicker, interrupt, sag, swell, notching, transient and combinations of these events. Comparison of test results with those generated by other existing methods shows enhanced performance with a classification accuracy of 98.18%. The main contribution of the paper is an accurate (because of proper selection of feature vectors), fast (e.g. a new de-noising approach with proposed identification criterion) and robust (at different signal-to-noise ratios) wavelet network-based algorithm (as compared to the conventional wavelet-based algorithms) for detection/classification of individual, as well as combined PQ disturbances. 2010 Journal Article http://hdl.handle.net/20.500.11937/17635 10.1049/iet-smt.2009.0006 Institution of Engineering and Technology restricted |
| spellingShingle | Masoum, Mohammad Jamali, S. Ghaffarzadeh, N. Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks |
| title | Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks |
| title_full | Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks |
| title_fullStr | Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks |
| title_full_unstemmed | Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks |
| title_short | Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks |
| title_sort | detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks |
| url | http://hdl.handle.net/20.500.11937/17635 |