A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation
A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relatio...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xia, Yili [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
Complex-valued least squares (CLS) |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on power delivery - New York, NY : IEEE, 1986, 32(2017), 3, Seite 1270-1278 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2017 ; number:3 ; pages:1270-1278 |
| Links: |
|---|
| DOI / URN: |
10.1109/TPWRD.2015.2418778 |
|---|
| Katalog-ID: |
OLC1995068322 |
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| 520 | |a A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. | ||
| 650 | 4 | |a Frequency estimation | |
| 650 | 4 | |a Harmonic analysis | |
| 650 | 4 | |a Power system harmonics | |
| 650 | 4 | |a Complex-valued least squares (CLS) | |
| 650 | 4 | |a Noise | |
| 650 | 4 | |a Estimation | |
| 650 | 4 | |a discrete Fourier transform (DFT) | |
| 650 | 4 | |a Discrete Fourier transforms | |
| 700 | 1 | |a He, Yukun |4 oth | |
| 700 | 1 | |a Wang, Kai |4 oth | |
| 700 | 1 | |a Pei, Wenjiang |4 oth | |
| 700 | 1 | |a Blazic, Zoran |4 oth | |
| 700 | 1 | |a Mandic, Danilo P |4 oth | |
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10.1109/TPWRD.2015.2418778 doi PQ20170721 (DE-627)OLC1995068322 (DE-599)GBVOLC1995068322 (PRQ)c1326-4a8e8e6a23117e31497695962f0c4a42b0533a18b74c8e8fac48446290e16f1b0 (KEY)0163644820170000032000301270complexleastsquaresenhancedsmartdfttechniqueforpow DE-627 ger DE-627 rakwb eng 620 DE-600 Xia, Yili verfasserin aut A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. Frequency estimation Harmonic analysis Power system harmonics Complex-valued least squares (CLS) Noise Estimation discrete Fourier transform (DFT) Discrete Fourier transforms He, Yukun oth Wang, Kai oth Pei, Wenjiang oth Blazic, Zoran oth Mandic, Danilo P oth Enthalten in IEEE transactions on power delivery New York, NY : IEEE, 1986 32(2017), 3, Seite 1270-1278 (DE-627)129382914 (DE-600)165807-4 (DE-576)014769328 0885-8977 nnns volume:32 year:2017 number:3 pages:1270-1278 http://dx.doi.org/10.1109/TPWRD.2015.2418778 Volltext http://ieeexplore.ieee.org/document/7086070 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2061 AR 32 2017 3 1270-1278 |
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10.1109/TPWRD.2015.2418778 doi PQ20170721 (DE-627)OLC1995068322 (DE-599)GBVOLC1995068322 (PRQ)c1326-4a8e8e6a23117e31497695962f0c4a42b0533a18b74c8e8fac48446290e16f1b0 (KEY)0163644820170000032000301270complexleastsquaresenhancedsmartdfttechniqueforpow DE-627 ger DE-627 rakwb eng 620 DE-600 Xia, Yili verfasserin aut A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. Frequency estimation Harmonic analysis Power system harmonics Complex-valued least squares (CLS) Noise Estimation discrete Fourier transform (DFT) Discrete Fourier transforms He, Yukun oth Wang, Kai oth Pei, Wenjiang oth Blazic, Zoran oth Mandic, Danilo P oth Enthalten in IEEE transactions on power delivery New York, NY : IEEE, 1986 32(2017), 3, Seite 1270-1278 (DE-627)129382914 (DE-600)165807-4 (DE-576)014769328 0885-8977 nnns volume:32 year:2017 number:3 pages:1270-1278 http://dx.doi.org/10.1109/TPWRD.2015.2418778 Volltext http://ieeexplore.ieee.org/document/7086070 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2061 AR 32 2017 3 1270-1278 |
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10.1109/TPWRD.2015.2418778 doi PQ20170721 (DE-627)OLC1995068322 (DE-599)GBVOLC1995068322 (PRQ)c1326-4a8e8e6a23117e31497695962f0c4a42b0533a18b74c8e8fac48446290e16f1b0 (KEY)0163644820170000032000301270complexleastsquaresenhancedsmartdfttechniqueforpow DE-627 ger DE-627 rakwb eng 620 DE-600 Xia, Yili verfasserin aut A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. Frequency estimation Harmonic analysis Power system harmonics Complex-valued least squares (CLS) Noise Estimation discrete Fourier transform (DFT) Discrete Fourier transforms He, Yukun oth Wang, Kai oth Pei, Wenjiang oth Blazic, Zoran oth Mandic, Danilo P oth Enthalten in IEEE transactions on power delivery New York, NY : IEEE, 1986 32(2017), 3, Seite 1270-1278 (DE-627)129382914 (DE-600)165807-4 (DE-576)014769328 0885-8977 nnns volume:32 year:2017 number:3 pages:1270-1278 http://dx.doi.org/10.1109/TPWRD.2015.2418778 Volltext http://ieeexplore.ieee.org/document/7086070 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2061 AR 32 2017 3 1270-1278 |
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10.1109/TPWRD.2015.2418778 doi PQ20170721 (DE-627)OLC1995068322 (DE-599)GBVOLC1995068322 (PRQ)c1326-4a8e8e6a23117e31497695962f0c4a42b0533a18b74c8e8fac48446290e16f1b0 (KEY)0163644820170000032000301270complexleastsquaresenhancedsmartdfttechniqueforpow DE-627 ger DE-627 rakwb eng 620 DE-600 Xia, Yili verfasserin aut A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. Frequency estimation Harmonic analysis Power system harmonics Complex-valued least squares (CLS) Noise Estimation discrete Fourier transform (DFT) Discrete Fourier transforms He, Yukun oth Wang, Kai oth Pei, Wenjiang oth Blazic, Zoran oth Mandic, Danilo P oth Enthalten in IEEE transactions on power delivery New York, NY : IEEE, 1986 32(2017), 3, Seite 1270-1278 (DE-627)129382914 (DE-600)165807-4 (DE-576)014769328 0885-8977 nnns volume:32 year:2017 number:3 pages:1270-1278 http://dx.doi.org/10.1109/TPWRD.2015.2418778 Volltext http://ieeexplore.ieee.org/document/7086070 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2061 AR 32 2017 3 1270-1278 |
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10.1109/TPWRD.2015.2418778 doi PQ20170721 (DE-627)OLC1995068322 (DE-599)GBVOLC1995068322 (PRQ)c1326-4a8e8e6a23117e31497695962f0c4a42b0533a18b74c8e8fac48446290e16f1b0 (KEY)0163644820170000032000301270complexleastsquaresenhancedsmartdfttechniqueforpow DE-627 ger DE-627 rakwb eng 620 DE-600 Xia, Yili verfasserin aut A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. Frequency estimation Harmonic analysis Power system harmonics Complex-valued least squares (CLS) Noise Estimation discrete Fourier transform (DFT) Discrete Fourier transforms He, Yukun oth Wang, Kai oth Pei, Wenjiang oth Blazic, Zoran oth Mandic, Danilo P oth Enthalten in IEEE transactions on power delivery New York, NY : IEEE, 1986 32(2017), 3, Seite 1270-1278 (DE-627)129382914 (DE-600)165807-4 (DE-576)014769328 0885-8977 nnns volume:32 year:2017 number:3 pages:1270-1278 http://dx.doi.org/10.1109/TPWRD.2015.2418778 Volltext http://ieeexplore.ieee.org/document/7086070 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2014 GBV_ILN_2016 GBV_ILN_2061 AR 32 2017 3 1270-1278 |
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ddc 620 misc Frequency estimation misc Harmonic analysis misc Power system harmonics misc Complex-valued least squares (CLS) misc Noise misc Estimation misc discrete Fourier transform (DFT) misc Discrete Fourier transforms |
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ddc 620 misc Frequency estimation misc Harmonic analysis misc Power system harmonics misc Complex-valued least squares (CLS) misc Noise misc Estimation misc discrete Fourier transform (DFT) misc Discrete Fourier transforms |
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A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation |
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A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation |
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Xia, Yili |
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IEEE transactions on power delivery |
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10.1109/TPWRD.2015.2418778 |
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complex least squares enhanced smart dft technique for power system frequency estimation |
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A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation |
| abstract |
A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. |
| abstractGer |
A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. |
| abstract_unstemmed |
A complex-valued least-squares (CLS) framework is proposed in order to enhance the accuracy of the smart discrete Fourier transform (SDFT) algorithms for power system frequency estimation in the presence of noise and harmonic pollution. It is first established that the underlying time-series relationship among the consecutive DFT fundamental components employed by the original SDFT algorithms does not hold when noises or unexpected higher order harmonics are present, resulting in suboptimal estimation performances. To eliminate these adverse effects on the frequency estimation, the degree of the relationship breakdown is next quantified via a model mismatch error vector. The CLS technique is then employed to minimize the mean-square model deviation when the SDFT voltage modelling is suboptimal. The proposed CLS-enhanced SDFT (CLS-SDFT) methods are shown to be more accurate than the original ones in heavily noisy and harmonic-distorted environments, typical scenarios in online frequency estimation. The benefits of the SDFT framework are verified by simulations for various power system conditions, as well as for real-world measurements. |
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A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation |
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http://dx.doi.org/10.1109/TPWRD.2015.2418778 http://ieeexplore.ieee.org/document/7086070 |
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He, Yukun Wang, Kai Pei, Wenjiang Blazic, Zoran Mandic, Danilo P |
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