Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method

Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVA<i>k</i>NN, for monitoring the disturbances of power transmissi...

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Autores principales:	Shubin Wang, Yukun Tian, Xiaogang Deng, Qianlei Cao, Lei Wang, Pengxiang Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	canonical variate analysis disturbance detection power transmission system <i>k</i>-nearest neighbor analysis statistical local analysis Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/4af2f4c10c4e48bb9856d70791c7c335
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spelling	oai:doaj.org-article:4af2f4c10c4e48bb9856d70791c7c3352021-11-25T18:12:12ZDisturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method10.3390/machines91102722075-1702https://doaj.org/article/4af2f4c10c4e48bb9856d70791c7c3352021-11-01T00:00:00Zhttps://www.mdpi.com/2075-1702/9/11/272https://doaj.org/toc/2075-1702Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVA<i>k</i>NN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>T</mi><mn>2</mn></msup></semantics></math></inline-formula> and <i>Q</i>. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the <i>k</i>-nearest neighbor (<i>k</i>NN) technique is applied to design the statistics nearest neighbor distance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><msup><mi>T</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>Q</mi></mrow></semantics></math></inline-formula> as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVA<i>k</i>NN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.Shubin WangYukun TianXiaogang DengQianlei CaoLei WangPengxiang SunMDPI AGarticlecanonical variate analysisdisturbance detectionpower transmission system<i>k</i>-nearest neighbor analysisstatistical local analysisMechanical engineering and machineryTJ1-1570ENMachines, Vol 9, Iss 272, p 272 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	canonical variate analysis disturbance detection power transmission system <i>k</i>-nearest neighbor analysis statistical local analysis Mechanical engineering and machinery TJ1-1570
spellingShingle	canonical variate analysis disturbance detection power transmission system <i>k</i>-nearest neighbor analysis statistical local analysis Mechanical engineering and machinery TJ1-1570 Shubin Wang Yukun Tian Xiaogang Deng Qianlei Cao Lei Wang Pengxiang Sun Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method
description	Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVA<i>k</i>NN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>T</mi><mn>2</mn></msup></semantics></math></inline-formula> and <i>Q</i>. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the <i>k</i>-nearest neighbor (<i>k</i>NN) technique is applied to design the statistics nearest neighbor distance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><msup><mi>T</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>Q</mi></mrow></semantics></math></inline-formula> as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVA<i>k</i>NN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.
format	article
author	Shubin Wang Yukun Tian Xiaogang Deng Qianlei Cao Lei Wang Pengxiang Sun
author_facet	Shubin Wang Yukun Tian Xiaogang Deng Qianlei Cao Lei Wang Pengxiang Sun
author_sort	Shubin Wang
title	Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method
title_short	Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method
title_full	Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method
title_fullStr	Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method
title_full_unstemmed	Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method
title_sort	disturbance detection of a power transmission system based on the enhanced canonical variate analysis method
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4af2f4c10c4e48bb9856d70791c7c335
work_keys_str_mv	AT shubinwang disturbancedetectionofapowertransmissionsystembasedontheenhancedcanonicalvariateanalysismethod AT yukuntian disturbancedetectionofapowertransmissionsystembasedontheenhancedcanonicalvariateanalysismethod AT xiaogangdeng disturbancedetectionofapowertransmissionsystembasedontheenhancedcanonicalvariateanalysismethod AT qianleicao disturbancedetectionofapowertransmissionsystembasedontheenhancedcanonicalvariateanalysismethod AT leiwang disturbancedetectionofapowertransmissionsystembasedontheenhancedcanonicalvariateanalysismethod AT pengxiangsun disturbancedetectionofapowertransmissionsystembasedontheenhancedcanonicalvariateanalysismethod
_version_	1718411496520679424

Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method

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