Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential Waveforms

Voltage transient disturbance is one of the key voltage quality disturbances, which can be classified into oscillatory transient and impulsive transient. Currently, there is no simple, accurate and general method to detect, extract, and characterize the voltage transient disturbance. This paper prop...

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Main Authors: Yuanqian Ma, Qiyuan Li, Hanzhong Chen, Haibo Li, Yi Lei
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9580922/
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author Yuanqian Ma
Qiyuan Li
Hanzhong Chen
Haibo Li
Yi Lei
author_facet Yuanqian Ma
Qiyuan Li
Hanzhong Chen
Haibo Li
Yi Lei
author_sort Yuanqian Ma
collection DOAJ
description Voltage transient disturbance is one of the key voltage quality disturbances, which can be classified into oscillatory transient and impulsive transient. Currently, there is no simple, accurate and general method to detect, extract, and characterize the voltage transient disturbance. This paper proposes a time-domain voltage transient disturbance detection method based on the RMS (root mean square) values of segmented differential waveforms. Firstly, the abnormal voltage waveforms are detected and extracted, which will be used for further analysis. Secondly, the extracted abnormal voltage waveform data are pre-processed, and then each power frequency cycle are processed in segment. Thirdly, by considering the influence of the system frequency variation, the differential waveform can be obtained by calculating the difference between two consecutive cycles. Finally, the voltage transient disturbance is detected by calculating and comparing the segmented RMS values of differential waveforms and steady-state waveforms. The transient component is extracted as well. Four indicators, i.e., dominant frequency, polarity, magnitude and duration, are used to characterize the voltage transient disturbance. In order to verify the accuracy and feasibility of the proposed method, both simulated data from an improved IEEE-13 node test system and actual voltage transient signal from a laboratory experiment are used.
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spelling doaj.art-185d789a01de41cc810a10770606ca572022-12-21T23:09:50ZengIEEEIEEE Access2169-35362021-01-01914451414452910.1109/ACCESS.2021.31215079580922Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential WaveformsYuanqian Ma0https://orcid.org/0000-0002-2460-113XQiyuan Li1Hanzhong Chen2Haibo Li3https://orcid.org/0000-0002-7192-7458Yi Lei4https://orcid.org/0000-0003-0582-5717Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, ChinaFaculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, ChinaFaculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, ChinaTsinghua Sichuan Energy Internet Research Institute, Chengdu, Sichuan, ChinaTsinghua Sichuan Energy Internet Research Institute, Chengdu, Sichuan, ChinaVoltage transient disturbance is one of the key voltage quality disturbances, which can be classified into oscillatory transient and impulsive transient. Currently, there is no simple, accurate and general method to detect, extract, and characterize the voltage transient disturbance. This paper proposes a time-domain voltage transient disturbance detection method based on the RMS (root mean square) values of segmented differential waveforms. Firstly, the abnormal voltage waveforms are detected and extracted, which will be used for further analysis. Secondly, the extracted abnormal voltage waveform data are pre-processed, and then each power frequency cycle are processed in segment. Thirdly, by considering the influence of the system frequency variation, the differential waveform can be obtained by calculating the difference between two consecutive cycles. Finally, the voltage transient disturbance is detected by calculating and comparing the segmented RMS values of differential waveforms and steady-state waveforms. The transient component is extracted as well. Four indicators, i.e., dominant frequency, polarity, magnitude and duration, are used to characterize the voltage transient disturbance. In order to verify the accuracy and feasibility of the proposed method, both simulated data from an improved IEEE-13 node test system and actual voltage transient signal from a laboratory experiment are used.https://ieeexplore.ieee.org/document/9580922/Voltage transient disturbancedifferential waveformsegmented RMS valuefrequency variationtransient component
spellingShingle Yuanqian Ma
Qiyuan Li
Hanzhong Chen
Haibo Li
Yi Lei
Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential Waveforms
IEEE Access
Voltage transient disturbance
differential waveform
segmented RMS value
frequency variation
transient component
title Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential Waveforms
title_full Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential Waveforms
title_fullStr Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential Waveforms
title_full_unstemmed Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential Waveforms
title_short Voltage Transient Disturbance Detection Based on the RMS Values of Segmented Differential Waveforms
title_sort voltage transient disturbance detection based on the rms values of segmented differential waveforms
topic Voltage transient disturbance
differential waveform
segmented RMS value
frequency variation
transient component
url https://ieeexplore.ieee.org/document/9580922/
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AT qiyuanli voltagetransientdisturbancedetectionbasedonthermsvaluesofsegmenteddifferentialwaveforms
AT hanzhongchen voltagetransientdisturbancedetectionbasedonthermsvaluesofsegmenteddifferentialwaveforms
AT haiboli voltagetransientdisturbancedetectionbasedonthermsvaluesofsegmenteddifferentialwaveforms
AT yilei voltagetransientdisturbancedetectionbasedonthermsvaluesofsegmenteddifferentialwaveforms