Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation

Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracti...
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Autor*in:	Hwanhee Cho [verfasserIn] Namki Choi [verfasserIn] Byongjun Lee [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Approximation method oscillation monitoring power system measurement subsynchronous oscillation time-series analysis

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 34375-34386
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:34375-34386

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2974259

Katalog-ID:	DOAJ004279395

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allfields	10.1109/ACCESS.2020.2974259 doi (DE-627)DOAJ004279395 (DE-599)DOAJfcfd5fb46d8448c48789a96a48e82652 DE-627 ger DE-627 rakwb eng TK1-9971 Hwanhee Cho verfasserin aut Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process. Approximation method oscillation monitoring power system measurement subsynchronous oscillation time-series analysis Electrical engineering. Electronics. Nuclear engineering Namki Choi verfasserin aut Byongjun Lee verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 34375-34386 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:34375-34386 https://doi.org/10.1109/ACCESS.2020.2974259 kostenfrei https://doaj.org/article/fcfd5fb46d8448c48789a96a48e82652 kostenfrei https://ieeexplore.ieee.org/document/9000587/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 34375-34386
spelling	10.1109/ACCESS.2020.2974259 doi (DE-627)DOAJ004279395 (DE-599)DOAJfcfd5fb46d8448c48789a96a48e82652 DE-627 ger DE-627 rakwb eng TK1-9971 Hwanhee Cho verfasserin aut Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process. Approximation method oscillation monitoring power system measurement subsynchronous oscillation time-series analysis Electrical engineering. Electronics. Nuclear engineering Namki Choi verfasserin aut Byongjun Lee verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 34375-34386 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:34375-34386 https://doi.org/10.1109/ACCESS.2020.2974259 kostenfrei https://doaj.org/article/fcfd5fb46d8448c48789a96a48e82652 kostenfrei https://ieeexplore.ieee.org/document/9000587/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 34375-34386
allfields_unstemmed	10.1109/ACCESS.2020.2974259 doi (DE-627)DOAJ004279395 (DE-599)DOAJfcfd5fb46d8448c48789a96a48e82652 DE-627 ger DE-627 rakwb eng TK1-9971 Hwanhee Cho verfasserin aut Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process. Approximation method oscillation monitoring power system measurement subsynchronous oscillation time-series analysis Electrical engineering. Electronics. Nuclear engineering Namki Choi verfasserin aut Byongjun Lee verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 34375-34386 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:34375-34386 https://doi.org/10.1109/ACCESS.2020.2974259 kostenfrei https://doaj.org/article/fcfd5fb46d8448c48789a96a48e82652 kostenfrei https://ieeexplore.ieee.org/document/9000587/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 34375-34386
allfieldsGer	10.1109/ACCESS.2020.2974259 doi (DE-627)DOAJ004279395 (DE-599)DOAJfcfd5fb46d8448c48789a96a48e82652 DE-627 ger DE-627 rakwb eng TK1-9971 Hwanhee Cho verfasserin aut Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process. Approximation method oscillation monitoring power system measurement subsynchronous oscillation time-series analysis Electrical engineering. Electronics. Nuclear engineering Namki Choi verfasserin aut Byongjun Lee verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 34375-34386 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:34375-34386 https://doi.org/10.1109/ACCESS.2020.2974259 kostenfrei https://doaj.org/article/fcfd5fb46d8448c48789a96a48e82652 kostenfrei https://ieeexplore.ieee.org/document/9000587/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 34375-34386
allfieldsSound	10.1109/ACCESS.2020.2974259 doi (DE-627)DOAJ004279395 (DE-599)DOAJfcfd5fb46d8448c48789a96a48e82652 DE-627 ger DE-627 rakwb eng TK1-9971 Hwanhee Cho verfasserin aut Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process. Approximation method oscillation monitoring power system measurement subsynchronous oscillation time-series analysis Electrical engineering. Electronics. Nuclear engineering Namki Choi verfasserin aut Byongjun Lee verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 34375-34386 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:34375-34386 https://doi.org/10.1109/ACCESS.2020.2974259 kostenfrei https://doaj.org/article/fcfd5fb46d8448c48789a96a48e82652 kostenfrei https://ieeexplore.ieee.org/document/9000587/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 34375-34386
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source	In IEEE Access 8(2020), Seite 34375-34386 volume:8 year:2020 pages:34375-34386
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authorswithroles_txt_mv	Hwanhee Cho @@aut@@ Namki Choi @@aut@@ Byongjun Lee @@aut@@
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callnumber-first	T - Technology
author	Hwanhee Cho
spellingShingle	Hwanhee Cho misc TK1-9971 misc Approximation method misc oscillation monitoring misc power system measurement misc subsynchronous oscillation misc time-series analysis misc Electrical engineering. Electronics. Nuclear engineering Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation
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topic_title	TK1-9971 Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation Approximation method oscillation monitoring power system measurement subsynchronous oscillation time-series analysis
topic	misc TK1-9971 misc Approximation method misc oscillation monitoring misc power system measurement misc subsynchronous oscillation misc time-series analysis misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Approximation method misc oscillation monitoring misc power system measurement misc subsynchronous oscillation misc time-series analysis misc Electrical engineering. Electronics. Nuclear engineering
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title	Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation
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title_sort	oscillation recognition using a geometric feature extraction process based on periodic time-series approximation
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title_auth	Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation
abstract	Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process.
abstractGer	Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process.
abstract_unstemmed	Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincare ́map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincare ́map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process.
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title_short	Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation
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