Power Quality Analysis Using Bilinear Time-Frequency Distributions
Bilinear time-frequency distributions (TFDs) are powerful techniques that offer good time and frequency resolution of time-frequency representation (TFR). It is very appropriate to analyze power quality signals which consist of nonstationary and multi-frequency components. However, the TFDs suffer f...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2011
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/9464/1/2010_Journal_Power_Quality_Analysis_Using_Bilinear_Time-Frequency_Distributions.pdf http://eprints.utem.edu.my/id/eprint/9464/ http://asp.eurasipjournals.com/content/pdf/1687-6180-2010-837360.pdf |
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| Summary: | Bilinear time-frequency distributions (TFDs) are powerful techniques that offer good time and frequency resolution of time-frequency representation (TFR). It is very appropriate to analyze power quality signals which consist of nonstationary and multi-frequency components. However, the TFDs suffer from interference because of cross-terms. Many TFDs have been implemented, and there is no fixed window or kernel that can remove the cross-terms for all types of signals. In this paper, the bilinear TFDs are implemented to analyze power quality signals such as smooth-windowed Wigner-Ville distribution (SWWVD), Choi-Williams distribution (CWD), B-distribution (BD), and modified B-distribution (MBD). The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std, 1159-1995. A set of performance measures is defined and used to compare the TFRs. It shows that SWWVD presents the best performance and is selected for power quality signal analysis. Thus, an adaptive optimal kernel SWWVD is designed to determine the separable kernel automatically from the input signal. |
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