Bad Data Detection Algorithm for PMU Based on Spectral Clustering

Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data qua...
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Autor*in:	Zhiwei Yang [verfasserIn] Hao Liu [verfasserIn] Tianshu Bi [verfasserIn] Qixun Yang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering

Übergeordnetes Werk:	In: Journal of Modern Power Systems and Clean Energy - IEEE, 2016, 8(2020), 3, Seite 473-483
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:3 ; pages:473-483

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.35833/MPCE.2019.000457

Katalog-ID:	DOAJ058330410

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520			\|a Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.
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allfields	10.35833/MPCE.2019.000457 doi (DE-627)DOAJ058330410 (DE-599)DOAJ6a1e281b16264970bd4778b5a6719ca1 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Zhiwei Yang verfasserin aut Bad Data Detection Algorithm for PMU Based on Spectral Clustering 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems. Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering Production of electric energy or power. Powerplants. Central stations Renewable energy sources Hao Liu verfasserin aut Tianshu Bi verfasserin aut Qixun Yang verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 3, Seite 473-483 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:3 pages:473-483 https://doi.org/10.35833/MPCE.2019.000457 kostenfrei https://doaj.org/article/6a1e281b16264970bd4778b5a6719ca1 kostenfrei https://ieeexplore.ieee.org/document/9062453/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 473-483
spelling	10.35833/MPCE.2019.000457 doi (DE-627)DOAJ058330410 (DE-599)DOAJ6a1e281b16264970bd4778b5a6719ca1 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Zhiwei Yang verfasserin aut Bad Data Detection Algorithm for PMU Based on Spectral Clustering 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems. Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering Production of electric energy or power. Powerplants. Central stations Renewable energy sources Hao Liu verfasserin aut Tianshu Bi verfasserin aut Qixun Yang verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 3, Seite 473-483 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:3 pages:473-483 https://doi.org/10.35833/MPCE.2019.000457 kostenfrei https://doaj.org/article/6a1e281b16264970bd4778b5a6719ca1 kostenfrei https://ieeexplore.ieee.org/document/9062453/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 473-483
allfields_unstemmed	10.35833/MPCE.2019.000457 doi (DE-627)DOAJ058330410 (DE-599)DOAJ6a1e281b16264970bd4778b5a6719ca1 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Zhiwei Yang verfasserin aut Bad Data Detection Algorithm for PMU Based on Spectral Clustering 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems. Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering Production of electric energy or power. Powerplants. Central stations Renewable energy sources Hao Liu verfasserin aut Tianshu Bi verfasserin aut Qixun Yang verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 3, Seite 473-483 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:3 pages:473-483 https://doi.org/10.35833/MPCE.2019.000457 kostenfrei https://doaj.org/article/6a1e281b16264970bd4778b5a6719ca1 kostenfrei https://ieeexplore.ieee.org/document/9062453/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 473-483
allfieldsGer	10.35833/MPCE.2019.000457 doi (DE-627)DOAJ058330410 (DE-599)DOAJ6a1e281b16264970bd4778b5a6719ca1 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Zhiwei Yang verfasserin aut Bad Data Detection Algorithm for PMU Based on Spectral Clustering 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems. Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering Production of electric energy or power. Powerplants. Central stations Renewable energy sources Hao Liu verfasserin aut Tianshu Bi verfasserin aut Qixun Yang verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 3, Seite 473-483 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:3 pages:473-483 https://doi.org/10.35833/MPCE.2019.000457 kostenfrei https://doaj.org/article/6a1e281b16264970bd4778b5a6719ca1 kostenfrei https://ieeexplore.ieee.org/document/9062453/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 473-483
allfieldsSound	10.35833/MPCE.2019.000457 doi (DE-627)DOAJ058330410 (DE-599)DOAJ6a1e281b16264970bd4778b5a6719ca1 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Zhiwei Yang verfasserin aut Bad Data Detection Algorithm for PMU Based on Spectral Clustering 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems. Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering Production of electric energy or power. Powerplants. Central stations Renewable energy sources Hao Liu verfasserin aut Tianshu Bi verfasserin aut Qixun Yang verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 3, Seite 473-483 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:3 pages:473-483 https://doi.org/10.35833/MPCE.2019.000457 kostenfrei https://doaj.org/article/6a1e281b16264970bd4778b5a6719ca1 kostenfrei https://ieeexplore.ieee.org/document/9062453/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 473-483
language	English
source	In Journal of Modern Power Systems and Clean Energy 8(2020), 3, Seite 473-483 volume:8 year:2020 number:3 pages:473-483
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institution	findex.gbv.de
topic_facet	Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering Production of electric energy or power. Powerplants. Central stations Renewable energy sources
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container_title	Journal of Modern Power Systems and Clean Energy
authorswithroles_txt_mv	Zhiwei Yang @@aut@@ Hao Liu @@aut@@ Tianshu Bi @@aut@@ Qixun Yang @@aut@@
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callnumber-first	T - Technology
author	Zhiwei Yang
spellingShingle	Zhiwei Yang misc TK1001-1841 misc TJ807-830 misc Phasor measurement units (PMUs) misc bad data detection misc event data identification misc decision tree misc spectral clustering misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources Bad Data Detection Algorithm for PMU Based on Spectral Clustering
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topic_title	TK1001-1841 TJ807-830 Bad Data Detection Algorithm for PMU Based on Spectral Clustering Phasor measurement units (PMUs) bad data detection event data identification decision tree spectral clustering
topic	misc TK1001-1841 misc TJ807-830 misc Phasor measurement units (PMUs) misc bad data detection misc event data identification misc decision tree misc spectral clustering misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
topic_unstemmed	misc TK1001-1841 misc TJ807-830 misc Phasor measurement units (PMUs) misc bad data detection misc event data identification misc decision tree misc spectral clustering misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
topic_browse	misc TK1001-1841 misc TJ807-830 misc Phasor measurement units (PMUs) misc bad data detection misc event data identification misc decision tree misc spectral clustering misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
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title	Bad Data Detection Algorithm for PMU Based on Spectral Clustering
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title_full	Bad Data Detection Algorithm for PMU Based on Spectral Clustering
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title_sort	bad data detection algorithm for pmu based on spectral clustering
callnumber	TK1001-1841
title_auth	Bad Data Detection Algorithm for PMU Based on Spectral Clustering
abstract	Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.
abstractGer	Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.
abstract_unstemmed	Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.
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title_short	Bad Data Detection Algorithm for PMU Based on Spectral Clustering
url	https://doi.org/10.35833/MPCE.2019.000457 https://doaj.org/article/6a1e281b16264970bd4778b5a6719ca1 https://ieeexplore.ieee.org/document/9062453/ https://doaj.org/toc/2196-5420
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doi_str	10.35833/MPCE.2019.000457
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up_date	2024-07-03T17:21:58.770Z
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