No Cover Image

Journal article 1154 views 280 downloads

Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data

Alvaro Furlani Bastos, Keng Weng Lao, Grazia Todeschini, Surya Santoso

IEEE Transactions on Power Delivery, Pages: 1 - 1

Swansea University Author: Grazia Todeschini

Check full text

DOI (Published version): 10.1109/TPWRD.2018.2831183

Abstract

The voluminous amount of raw waveform data recorded by triggerless power quality monitors contain conspicuous and inconspicuous disturbance events. Data reduction and detection techniques are needed to efficiently extract useful information hidden in the raw data and identify power quality disturban...

Full description

Published in: IEEE Transactions on Power Delivery
ISSN: 0885-8977 1937-4208
Published: 2018
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa39692
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2018-05-01T14:27:12Z
last_indexed 2020-06-18T12:55:38Z
id cronfa39692
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2020-06-18T12:29:29.4630479</datestamp><bib-version>v2</bib-version><id>39692</id><entry>2018-05-01</entry><title>Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data</title><swanseaauthors><author><sid>c4ff9050b31bdec0e560b19bfb3b56d3</sid><firstname>Grazia</firstname><surname>Todeschini</surname><name>Grazia Todeschini</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2018-05-01</date><deptcode>FGSEN</deptcode><abstract>The voluminous amount of raw waveform data recorded by triggerless power quality monitors contain conspicuous and inconspicuous disturbance events. Data reduction and detection techniques are needed to efficiently extract useful information hidden in the raw data and identify power quality disturbances. The overall objective of this study is to use step changes in the rms voltage profile as an alternative triggering feature for automatically detecting switching events. The full characterization of the event is based on processing a small portion of the voltage waveform selected around the detected rms voltage step change. A filtering method is proposed to smooth out rapid fluctuations in the rms voltage profile during steady-state operation, while preserving the sharp edges caused by rms voltage step changes. Once the rms voltage profile has been filtered, adaptive limits based on the median absolute deviation are computed for detecting rms voltage step changes. The effectiveness of the proposed technique is evaluated using triggerless voltage waveforms to detect capacitor switching events. The use of the filtered rms voltage profile allows accurate detection of capacitor energizing and de-energizing events, while more than 50% of the detections in the unfiltered profile correspond to false-positives</abstract><type>Journal Article</type><journal>IEEE Transactions on Power Delivery</journal><paginationStart>1</paginationStart><paginationEnd>1</paginationEnd><publisher/><issnPrint>0885-8977</issnPrint><issnElectronic>1937-4208</issnElectronic><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-12-31</publishedDate><doi>10.1109/TPWRD.2018.2831183</doi><url/><notes/><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-06-18T12:29:29.4630479</lastEdited><Created>2018-05-01T08:45:41.0423989</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>Alvaro Furlani</firstname><surname>Bastos</surname><order>1</order></author><author><firstname>Keng Weng</firstname><surname>Lao</surname><order>2</order></author><author><firstname>Grazia</firstname><surname>Todeschini</surname><order>3</order></author><author><firstname>Surya</firstname><surname>Santoso</surname><order>4</order></author></authors><documents><document><filename>0039692-01052018084752.pdf</filename><originalFilename>bastos2018.pdf</originalFilename><uploaded>2018-05-01T08:47:52.0330000</uploaded><type>Output</type><contentLength>749446</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2018-05-01T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2020-06-18T12:29:29.4630479 v2 39692 2018-05-01 Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data c4ff9050b31bdec0e560b19bfb3b56d3 Grazia Todeschini Grazia Todeschini true false 2018-05-01 FGSEN The voluminous amount of raw waveform data recorded by triggerless power quality monitors contain conspicuous and inconspicuous disturbance events. Data reduction and detection techniques are needed to efficiently extract useful information hidden in the raw data and identify power quality disturbances. The overall objective of this study is to use step changes in the rms voltage profile as an alternative triggering feature for automatically detecting switching events. The full characterization of the event is based on processing a small portion of the voltage waveform selected around the detected rms voltage step change. A filtering method is proposed to smooth out rapid fluctuations in the rms voltage profile during steady-state operation, while preserving the sharp edges caused by rms voltage step changes. Once the rms voltage profile has been filtered, adaptive limits based on the median absolute deviation are computed for detecting rms voltage step changes. The effectiveness of the proposed technique is evaluated using triggerless voltage waveforms to detect capacitor switching events. The use of the filtered rms voltage profile allows accurate detection of capacitor energizing and de-energizing events, while more than 50% of the detections in the unfiltered profile correspond to false-positives Journal Article IEEE Transactions on Power Delivery 1 1 0885-8977 1937-4208 31 12 2018 2018-12-31 10.1109/TPWRD.2018.2831183 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2020-06-18T12:29:29.4630479 2018-05-01T08:45:41.0423989 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Alvaro Furlani Bastos 1 Keng Weng Lao 2 Grazia Todeschini 3 Surya Santoso 4 0039692-01052018084752.pdf bastos2018.pdf 2018-05-01T08:47:52.0330000 Output 749446 application/pdf Accepted Manuscript true 2018-05-01T00:00:00.0000000 true eng
title Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data
spellingShingle Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data
Grazia Todeschini
title_short Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data
title_full Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data
title_fullStr Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data
title_full_unstemmed Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data
title_sort Novel Moving Average Filter for Detecting rms Voltage Step Changes in Triggerless PQ Data
author_id_str_mv c4ff9050b31bdec0e560b19bfb3b56d3
author_id_fullname_str_mv c4ff9050b31bdec0e560b19bfb3b56d3_***_Grazia Todeschini
author Grazia Todeschini
author2 Alvaro Furlani Bastos
Keng Weng Lao
Grazia Todeschini
Surya Santoso
format Journal article
container_title IEEE Transactions on Power Delivery
container_start_page 1
publishDate 2018
institution Swansea University
issn 0885-8977
1937-4208
doi_str_mv 10.1109/TPWRD.2018.2831183
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description The voluminous amount of raw waveform data recorded by triggerless power quality monitors contain conspicuous and inconspicuous disturbance events. Data reduction and detection techniques are needed to efficiently extract useful information hidden in the raw data and identify power quality disturbances. The overall objective of this study is to use step changes in the rms voltage profile as an alternative triggering feature for automatically detecting switching events. The full characterization of the event is based on processing a small portion of the voltage waveform selected around the detected rms voltage step change. A filtering method is proposed to smooth out rapid fluctuations in the rms voltage profile during steady-state operation, while preserving the sharp edges caused by rms voltage step changes. Once the rms voltage profile has been filtered, adaptive limits based on the median absolute deviation are computed for detecting rms voltage step changes. The effectiveness of the proposed technique is evaluated using triggerless voltage waveforms to detect capacitor switching events. The use of the filtered rms voltage profile allows accurate detection of capacitor energizing and de-energizing events, while more than 50% of the detections in the unfiltered profile correspond to false-positives
published_date 2018-12-31T03:50:28Z
_version_ 1763752467528417280
score 11.036334

Similar Items