Widely linear least mean kurtosis-based frequency estimation of three-phase power system
We propose a widely linear (augmented) least mean kurtosis (WL-LMK) algorithm for robust frequency estimation of three-phase power system. The negated kurtosis-based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can...
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| Published in | IET generation, transmission & distribution Vol. 14; no. 6; pp. 1159 - 1167 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
The Institution of Engineering and Technology
27.03.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-8687 1751-8695 |
| DOI | 10.1049/iet-gtd.2018.6498 |
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| Abstract | We propose a widely linear (augmented) least mean kurtosis (WL-LMK) algorithm for robust frequency estimation of three-phase power system. The negated kurtosis-based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can overcome the inherent performance degradation faced by the well-known minimum mean square error-based algorithms in noisy environments. The proposed widely linear LMK estimation technique utilises all second-order statistical information in the complex domain ${\opf C}$C for processing of non-circular complex-valued signals. The three-phase power system signal, modelled through Clarke's αβ transformation, is circular for balanced and non-circular for unbalanced systems, based on which, the proposed WL-LMK algorithm is able to achieve improved frequency estimation under unbalanced and other abnormal system conditions. Its estimation performance is evaluated for several cases that encounter in the day-to-day operation of power system. It is observed from simulation studies of synthetic and real-world power system data that the proposed WL-LMK algorithm exhibits superior estimation performance as compared to the standard linear complex LMK (CLMK) and the widely linear least mean square (WL-LMS) algorithms. |
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| AbstractList | We propose a widely linear (augmented) least mean kurtosis (WL-LMK) algorithm for robust frequency estimation of three-phase power system. The negated kurtosis-based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can overcome the inherent performance degradation faced by the well-known minimum mean square error-based algorithms in noisy environments. The proposed widely linear LMK estimation technique utilises all second-order statistical information in the complex domain ${\opf C}$C for processing of non-circular complex-valued signals. The three-phase power system signal, modelled through Clarke's αβ transformation, is circular for balanced and non-circular for unbalanced systems, based on which, the proposed WL-LMK algorithm is able to achieve improved frequency estimation under unbalanced and other abnormal system conditions. Its estimation performance is evaluated for several cases that encounter in the day-to-day operation of power system. It is observed from simulation studies of synthetic and real-world power system data that the proposed WL-LMK algorithm exhibits superior estimation performance as compared to the standard linear complex LMK (CLMK) and the widely linear least mean square (WL-LMS) algorithms. We propose a widely linear (augmented) least mean kurtosis (WL‐LMK) algorithm for robust frequency estimation of three‐phase power system. The negated kurtosis‐based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can overcome the inherent performance degradation faced by the well‐known minimum mean square error‐based algorithms in noisy environments. The proposed widely linear LMK estimation technique utilises all second‐order statistical information in the complex domain for processing of non‐circular complex‐valued signals. The three‐phase power system signal, modelled through Clarke's αβ transformation, is circular for balanced and non‐circular for unbalanced systems, based on which, the proposed WL‐LMK algorithm is able to achieve improved frequency estimation under unbalanced and other abnormal system conditions. Its estimation performance is evaluated for several cases that encounter in the day‐to‐day operation of power system. It is observed from simulation studies of synthetic and real‐world power system data that the proposed WL‐LMK algorithm exhibits superior estimation performance as compared to the standard linear complex LMK (CLMK) and the widely linear least mean square (WL‐LMS) algorithms. We propose a widely linear (augmented) least mean kurtosis (WL‐LMK) algorithm for robust frequency estimation of three‐phase power system. The negated kurtosis‐based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can overcome the inherent performance degradation faced by the well‐known minimum mean square error‐based algorithms in noisy environments. The proposed widely linear LMK estimation technique utilises all second‐order statistical information in the complex domain C for processing of non‐circular complex‐valued signals. The three‐phase power system signal, modelled through Clarke's αβ transformation, is circular for balanced and non‐circular for unbalanced systems, based on which, the proposed WL‐LMK algorithm is able to achieve improved frequency estimation under unbalanced and other abnormal system conditions. Its estimation performance is evaluated for several cases that encounter in the day‐to‐day operation of power system. It is observed from simulation studies of synthetic and real‐world power system data that the proposed WL‐LMK algorithm exhibits superior estimation performance as compared to the standard linear complex LMK (CLMK) and the widely linear least mean square (WL‐LMS) algorithms. |
| Author | Xia, Yili Zhao, Haiquan Nefabas, Gebeyehu L |
| Author_xml | – sequence: 1 givenname: Gebeyehu L orcidid: 0000-0002-0749-0706 surname: Nefabas fullname: Nefabas, Gebeyehu L organization: 1School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, People's Republic of China – sequence: 2 givenname: Haiquan surname: Zhao fullname: Zhao, Haiquan email: hqzhao@swjtu.edu.cn organization: 1School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, People's Republic of China – sequence: 3 givenname: Yili surname: Xia fullname: Xia, Yili organization: 2School of Information Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China |
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| CitedBy_id | crossref_primary_10_1109_TSP_2020_3029959 crossref_primary_10_1109_TSP_2021_3119777 crossref_primary_10_1016_j_sigpro_2022_108792 crossref_primary_10_3389_fenrg_2024_1467637 crossref_primary_10_1109_TPWRD_2022_3197814 |
| Cites_doi | 10.1109/19.650792 10.1049/el:19940129 10.1109/TPWRD.2003.822544 10.1109/MSP.2012.2183689 10.1002/9780470742624 10.1109/ENERGYCON.2014.6850513 10.1109/TIM.2003.817152 10.1049/el:19990243 10.1007/s00034-016-0379-3 10.1109/TSP.2017.2768024 10.1109/PROC.1975.9807 10.1109/61.634188 10.1109/TBME.2011.2173936 10.1109/TIM.2011.2159409 10.1109/19.863918 10.1016/j.ijepes.2013.07.030 10.1109/TSP.2009.2022353 10.1109/TIM.2002.1017717 10.1016/j.dsp.2016.03.012 10.1109/LSP.2010.2040929 10.1016/j.sigpro.2014.11.018 10.1016/S0378-7796(00)00080-8 10.1109/78.286978 10.1109/61.736681 10.1109/61.216849 10.1109/TPWRD.2003.822957 10.1049/iet-smt.2015.0018 |
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| Copyright | The Institution of Engineering and Technology 2020 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | WL-LMK algorithm exhibits superior estimation performance least mean squares methods power system simulation real-world power system data widely linear complex least mean kurtosis unbalanced systems standard linear complex LMK three-phase power system signal kurtosis-based algorithms frequency estimation abnormal system conditions second-order statistical information widely linear least mean square linear least mean kurtosis-based frequency estimation robust frequency estimation widely linear LMK estimation technique noise signals minimum mean square error-based algorithms unbalanced system conditions |
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| Snippet | We propose a widely linear (augmented) least mean kurtosis (WL-LMK) algorithm for robust frequency estimation of three-phase power system. The negated... We propose a widely linear (augmented) least mean kurtosis (WL‐LMK) algorithm for robust frequency estimation of three‐phase power system. The negated... |
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| SubjectTerms | abnormal system conditions Case Study frequency estimation kurtosis‐based algorithms least mean squares methods linear least mean kurtosis‐based frequency estimation minimum mean square error‐based algorithms noise signals power system simulation real‐world power system data robust frequency estimation second‐order statistical information standard linear complex LMK three‐phase power system signal unbalanced system conditions unbalanced systems widely linear complex least mean kurtosis widely linear least mean square widely linear LMK estimation technique WL‐LMK algorithm exhibits superior estimation performance |
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| Title | Widely linear least mean kurtosis-based frequency estimation of three-phase power system |
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