Diagnostic approach in assessment of a ferroresonant circuit View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-23

AUTHORS

Łukasz Majka, Maciej Klimas

ABSTRACT

This paper presents possibilities offered by a diagnostic system called FeD. The system is completely original; it has been developed by the authors on the basis of Arduino platform. The system has been designed to perform and record measurements and to carry out different numerical operations. The real-time function for several operations is incorporated in this system. The necessary input data for the system consist of the electrical voltage waveforms only. Rescaled voltage quantities can be displayed, measured, recorded or computed in any chosen way. The system has been developed particularly for measurements and computations in the ferroresonant circuits. The strongest part of the system is its versatility. It works with a standard PC and supports a universal connection (USB standard). This is undeniably a cost-wise solution. Driving and control of the system functions are carried out using the authors’ original software implemented in SciLab environment. This is free software, similar to and compatible with other existing CAD programs such as Octave and MATLAB. The obtained data, scripts and results can be freely transferred between them. The program is equipped with a transparent GUI. The need of constructing a special system to diagnose the ferroresonant circuit has emerged during earlier ferroresonance analyses and computations. Every ferroresonant circuit requires specific kind of diagnostics to estimate and display its base features in order to determine the best scientific approach to the problem. The ferroresonance phenomenon belongs to the domain of nonlinear problems. Its analysis requires excellent skills in mathematics and physics as well as computer science. Moreover, this subject also requires specialized engineering knowledge, particularly in the field of power engineering and power system equipment. Modern mathematical models and analyses used in ferroresonant computations are quite accurate; however, in case of a common user, they are often difficult to understand or implement. This paper provides full description of construction, features and test results of the developed hardware/software system designed for diagnostics of ferroresonant circuits. The test circuit case study has been performed in the entire power supply range. Results of measurements and computations as well as screenshots captured from authors’ original software are shown in different figures. The developed software and recorded data have been finally used in modeling and further simulations. During this, the application of the fractional derivative iron core coil model to ferroresonance analysis has been shown. The waveforms obtained from computer simulations have been compared with those obtained from measurements performed in the test circuit. More... »

PAGES

1-16

References to SciGraph publications

  • 2009-08. Modeling of nonlinear coil in a ferroresonant circuit in ELECTRICAL ENGINEERING
  • 2004. Neural Approach to Time-Frequency Signal Decomposition in ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2004
  • 2019. Fractional Derivative Approach in Modeling of a Nonlinear Coil for Ferroresonance Analyses in NON-INTEGER ORDER CALCULUS AND ITS APPLICATIONS
  • 2018-11. A Harmonic Balance Methodology for Circuits with Fractional and Nonlinear Elements in CIRCUITS, SYSTEMS, AND SIGNAL PROCESSING
  • 2017. Implementation of Bi-fractional Filtering on the Arduino Uno Hardware Platform in THEORY AND APPLICATIONS OF NON-INTEGER ORDER SYSTEMS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00202-019-00761-5

    DOI

    http://dx.doi.org/10.1007/s00202-019-00761-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112966456


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0803", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Computer Software", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Silesian University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.6979.1", 
              "name": [
                "Silesian University of Technology, Akademicka 10, 44-100, Gliwice, Poland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Majka", 
            "givenName": "\u0141ukasz", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Silesian University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.6979.1", 
              "name": [
                "Silesian University of Technology, Akademicka 10, 44-100, Gliwice, Poland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Klimas", 
            "givenName": "Maciej", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-540-24844-6_175", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001754970", 
              "https://doi.org/10.1007/978-3-540-24844-6_175"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-24844-6_175", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001754970", 
              "https://doi.org/10.1007/978-3-540-24844-6_175"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1108/compel-01-2013-0013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002109830"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0378-7796(98)00117-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002761328"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.infrared.2015.06.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004796146"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/0022-3727/41/4/045001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006432910"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijepes.2010.08.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006685006"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1108/03321641311317130", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010370912"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijepes.2014.03.036", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012236762"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.matcom.2013.09.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012533294"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmatprotec.2004.07.019", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015173340"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmmm.2006.03.046", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015956086"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2478/bpasts-2014-0047", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025405603"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijepes.2008.10.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029335956"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2014.12.013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029722019"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0304-8853(02)01463-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034576837"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00202-009-0117-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036815176", 
              "https://doi.org/10.1007/s00202-009-0117-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00202-009-0117-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036815176", 
              "https://doi.org/10.1007/s00202-009-0117-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00202-009-0117-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036815176", 
              "https://doi.org/10.1007/s00202-009-0117-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0142-0615(95)00045-r", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043862670"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2478/amm-2014-0094", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044028576"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijepes.2011.06.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044619700"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1515/aee-2016-0038", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050685257"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2478/bpasts-2013-0059", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053591695"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1049/ip-c.1993.0035", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056843816"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1063/1.4747915", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058058429"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/61.131146", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061198924"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/61.296285", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061199729"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/61.847260", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061201401"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/61.905606", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061201668"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmag.2013.2243908", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061685699"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpwrd.2008.2002668", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061772643"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpwrd.2014.2320599", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061774649"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.17981/ingecuc.11.1.2015.03", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068550986"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3906/elk-1310-129", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1071567927"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.5923/j.eee.20120202.10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1073508717"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.7763/ijcee.2012.v4.606", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1074032195"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2017.01.047", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083822818"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2017.02.028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084057199"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-45474-0_37", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084918008", 
              "https://doi.org/10.1007/978-3-319-45474-0_37"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/elps.201600551", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085120097"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.24084/repqj10.351", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085353250"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijepes.2017.05.026", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085862916"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2017.06.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086090353"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.24084/repqj05.317", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090844839"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.17694/bajece.337936", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091680560"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.15598/aeee.v15i3.2196", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092071187"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.15598/aeee.v15i3.2196", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092071187"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/i2mtc.2013.6555402", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093187998"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/atee.2017.7905167", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094114061"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/pes.2003.1270499", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094920189"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tencon.2008.4766386", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094986504"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/eeeic.2013.6549616", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095194514"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1090/gsm/140", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098735254"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3390/math6020016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100616323"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/1361-6463/aab0e1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101132108"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00034-018-0794-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101373801", 
              "https://doi.org/10.1007/s00034-018-0794-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00034-018-0794-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101373801", 
              "https://doi.org/10.1007/s00034-018-0794-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00034-018-0794-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101373801", 
              "https://doi.org/10.1007/s00034-018-0794-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1101409827", 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-78458-8_13", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101676942", 
              "https://doi.org/10.1007/978-3-319-78458-8_13"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1103285047", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iiphdw.2018.8388332", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105032066"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2018.05.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105051438"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmmm.2018.08.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106087154"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jfranklin.2018.08.017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106334160"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1107041725", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/9781119307181", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107041725"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/9781119307181", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107041725"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mmar.2018.8485964", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107559184"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijepes.2018.10.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107640013"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-03-23", 
        "datePublishedReg": "2019-03-23", 
        "description": "This paper presents possibilities offered by a diagnostic system called FeD. The system is completely original; it has been developed by the authors on the basis of Arduino platform. The system has been designed to perform and record measurements and to carry out different numerical operations. The real-time function for several operations is incorporated in this system. The necessary input data for the system consist of the electrical voltage waveforms only. Rescaled voltage quantities can be displayed, measured, recorded or computed in any chosen way. The system has been developed particularly for measurements and computations in the ferroresonant circuits. The strongest part of the system is its versatility. It works with a standard PC and supports a universal connection (USB standard). This is undeniably a cost-wise solution. Driving and control of the system functions are carried out using the authors\u2019 original software implemented in SciLab environment. This is free software, similar to and compatible with other existing CAD programs such as Octave and MATLAB. The obtained data, scripts and results can be freely transferred between them. The program is equipped with a transparent GUI. The need of constructing a special system to diagnose the ferroresonant circuit has emerged during earlier ferroresonance analyses and computations. Every ferroresonant circuit requires specific kind of diagnostics to estimate and display its base features in order to determine the best scientific approach to the problem. The ferroresonance phenomenon belongs to the domain of nonlinear problems. Its analysis requires excellent skills in mathematics and physics as well as computer science. Moreover, this subject also requires specialized engineering knowledge, particularly in the field of power engineering and power system equipment. Modern mathematical models and analyses used in ferroresonant computations are quite accurate; however, in case of a common user, they are often difficult to understand or implement. This paper provides full description of construction, features and test results of the developed hardware/software system designed for diagnostics of ferroresonant circuits. The test circuit case study has been performed in the entire power supply range. Results of measurements and computations as well as screenshots captured from authors\u2019 original software are shown in different figures. The developed software and recorded data have been finally used in modeling and further simulations. During this, the application of the fractional derivative iron core coil model to ferroresonance analysis has been shown. The waveforms obtained from computer simulations have been compared with those obtained from measurements performed in the test circuit.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00202-019-00761-5", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1136871", 
            "issn": [
              "0095-9197", 
              "2376-7804"
            ], 
            "name": "Electrical Engineering", 
            "type": "Periodical"
          }
        ], 
        "name": "Diagnostic approach in assessment of a ferroresonant circuit", 
        "pagination": "1-16", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "fe6002ccea625fc4b4f3f7dce779ce22261c32cbfeec5bb36382b45229c6502a"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00202-019-00761-5"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112966456"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00202-019-00761-5", 
          "https://app.dimensions.ai/details/publication/pub.1112966456"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:01", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000365_0000000365/records_71714_00000002.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00202-019-00761-5"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00202-019-00761-5'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00202-019-00761-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00202-019-00761-5'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00202-019-00761-5'


     

    This table displays all metadata directly associated to this object as RDF triples.

    254 TRIPLES      21 PREDICATES      88 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00202-019-00761-5 schema:about anzsrc-for:08
    2 anzsrc-for:0803
    3 schema:author N60b9a137614340d3acce8ab982c62431
    4 schema:citation sg:pub.10.1007/978-3-319-45474-0_37
    5 sg:pub.10.1007/978-3-319-78458-8_13
    6 sg:pub.10.1007/978-3-540-24844-6_175
    7 sg:pub.10.1007/s00034-018-0794-8
    8 sg:pub.10.1007/s00202-009-0117-y
    9 https://app.dimensions.ai/details/publication/pub.1101409827
    10 https://app.dimensions.ai/details/publication/pub.1103285047
    11 https://app.dimensions.ai/details/publication/pub.1107041725
    12 https://doi.org/10.1002/9781119307181
    13 https://doi.org/10.1002/elps.201600551
    14 https://doi.org/10.1016/0142-0615(95)00045-r
    15 https://doi.org/10.1016/j.amc.2014.12.013
    16 https://doi.org/10.1016/j.amc.2017.01.047
    17 https://doi.org/10.1016/j.amc.2017.02.028
    18 https://doi.org/10.1016/j.amc.2017.06.004
    19 https://doi.org/10.1016/j.amc.2018.05.005
    20 https://doi.org/10.1016/j.ijepes.2008.10.015
    21 https://doi.org/10.1016/j.ijepes.2010.08.003
    22 https://doi.org/10.1016/j.ijepes.2011.06.004
    23 https://doi.org/10.1016/j.ijepes.2014.03.036
    24 https://doi.org/10.1016/j.ijepes.2017.05.026
    25 https://doi.org/10.1016/j.ijepes.2018.10.011
    26 https://doi.org/10.1016/j.infrared.2015.06.002
    27 https://doi.org/10.1016/j.jfranklin.2018.08.017
    28 https://doi.org/10.1016/j.jmatprotec.2004.07.019
    29 https://doi.org/10.1016/j.jmmm.2006.03.046
    30 https://doi.org/10.1016/j.jmmm.2018.08.003
    31 https://doi.org/10.1016/j.matcom.2013.09.012
    32 https://doi.org/10.1016/s0304-8853(02)01463-4
    33 https://doi.org/10.1016/s0378-7796(98)00117-5
    34 https://doi.org/10.1049/ip-c.1993.0035
    35 https://doi.org/10.1063/1.4747915
    36 https://doi.org/10.1088/0022-3727/41/4/045001
    37 https://doi.org/10.1088/1361-6463/aab0e1
    38 https://doi.org/10.1090/gsm/140
    39 https://doi.org/10.1108/03321641311317130
    40 https://doi.org/10.1108/compel-01-2013-0013
    41 https://doi.org/10.1109/61.131146
    42 https://doi.org/10.1109/61.296285
    43 https://doi.org/10.1109/61.847260
    44 https://doi.org/10.1109/61.905606
    45 https://doi.org/10.1109/atee.2017.7905167
    46 https://doi.org/10.1109/eeeic.2013.6549616
    47 https://doi.org/10.1109/i2mtc.2013.6555402
    48 https://doi.org/10.1109/iiphdw.2018.8388332
    49 https://doi.org/10.1109/mmar.2018.8485964
    50 https://doi.org/10.1109/pes.2003.1270499
    51 https://doi.org/10.1109/tencon.2008.4766386
    52 https://doi.org/10.1109/tmag.2013.2243908
    53 https://doi.org/10.1109/tpwrd.2008.2002668
    54 https://doi.org/10.1109/tpwrd.2014.2320599
    55 https://doi.org/10.1515/aee-2016-0038
    56 https://doi.org/10.15598/aeee.v15i3.2196
    57 https://doi.org/10.17694/bajece.337936
    58 https://doi.org/10.17981/ingecuc.11.1.2015.03
    59 https://doi.org/10.24084/repqj05.317
    60 https://doi.org/10.24084/repqj10.351
    61 https://doi.org/10.2478/amm-2014-0094
    62 https://doi.org/10.2478/bpasts-2013-0059
    63 https://doi.org/10.2478/bpasts-2014-0047
    64 https://doi.org/10.3390/math6020016
    65 https://doi.org/10.3906/elk-1310-129
    66 https://doi.org/10.5923/j.eee.20120202.10
    67 https://doi.org/10.7763/ijcee.2012.v4.606
    68 schema:datePublished 2019-03-23
    69 schema:datePublishedReg 2019-03-23
    70 schema:description This paper presents possibilities offered by a diagnostic system called FeD. The system is completely original; it has been developed by the authors on the basis of Arduino platform. The system has been designed to perform and record measurements and to carry out different numerical operations. The real-time function for several operations is incorporated in this system. The necessary input data for the system consist of the electrical voltage waveforms only. Rescaled voltage quantities can be displayed, measured, recorded or computed in any chosen way. The system has been developed particularly for measurements and computations in the ferroresonant circuits. The strongest part of the system is its versatility. It works with a standard PC and supports a universal connection (USB standard). This is undeniably a cost-wise solution. Driving and control of the system functions are carried out using the authors’ original software implemented in SciLab environment. This is free software, similar to and compatible with other existing CAD programs such as Octave and MATLAB. The obtained data, scripts and results can be freely transferred between them. The program is equipped with a transparent GUI. The need of constructing a special system to diagnose the ferroresonant circuit has emerged during earlier ferroresonance analyses and computations. Every ferroresonant circuit requires specific kind of diagnostics to estimate and display its base features in order to determine the best scientific approach to the problem. The ferroresonance phenomenon belongs to the domain of nonlinear problems. Its analysis requires excellent skills in mathematics and physics as well as computer science. Moreover, this subject also requires specialized engineering knowledge, particularly in the field of power engineering and power system equipment. Modern mathematical models and analyses used in ferroresonant computations are quite accurate; however, in case of a common user, they are often difficult to understand or implement. This paper provides full description of construction, features and test results of the developed hardware/software system designed for diagnostics of ferroresonant circuits. The test circuit case study has been performed in the entire power supply range. Results of measurements and computations as well as screenshots captured from authors’ original software are shown in different figures. The developed software and recorded data have been finally used in modeling and further simulations. During this, the application of the fractional derivative iron core coil model to ferroresonance analysis has been shown. The waveforms obtained from computer simulations have been compared with those obtained from measurements performed in the test circuit.
    71 schema:genre research_article
    72 schema:inLanguage en
    73 schema:isAccessibleForFree false
    74 schema:isPartOf sg:journal.1136871
    75 schema:name Diagnostic approach in assessment of a ferroresonant circuit
    76 schema:pagination 1-16
    77 schema:productId N0c39e3684c62429389ff11d4f4465f2d
    78 Na881fa9459ce4625bca89829480bf00f
    79 Nd4f3079304ea4704a2e03a981d300d18
    80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112966456
    81 https://doi.org/10.1007/s00202-019-00761-5
    82 schema:sdDatePublished 2019-04-11T13:01
    83 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    84 schema:sdPublisher N26f2c6c813864fbfba27d960e5e87ca7
    85 schema:url https://link.springer.com/10.1007%2Fs00202-019-00761-5
    86 sgo:license sg:explorer/license/
    87 sgo:sdDataset articles
    88 rdf:type schema:ScholarlyArticle
    89 N071a27b361b145938851c2f685e19516 schema:affiliation https://www.grid.ac/institutes/grid.6979.1
    90 schema:familyName Klimas
    91 schema:givenName Maciej
    92 rdf:type schema:Person
    93 N0c39e3684c62429389ff11d4f4465f2d schema:name dimensions_id
    94 schema:value pub.1112966456
    95 rdf:type schema:PropertyValue
    96 N20b684e05da444b1941a210289b7e82c rdf:first N071a27b361b145938851c2f685e19516
    97 rdf:rest rdf:nil
    98 N26f2c6c813864fbfba27d960e5e87ca7 schema:name Springer Nature - SN SciGraph project
    99 rdf:type schema:Organization
    100 N60b9a137614340d3acce8ab982c62431 rdf:first N66bb1c149cae477eb9f4176330503148
    101 rdf:rest N20b684e05da444b1941a210289b7e82c
    102 N66bb1c149cae477eb9f4176330503148 schema:affiliation https://www.grid.ac/institutes/grid.6979.1
    103 schema:familyName Majka
    104 schema:givenName Łukasz
    105 rdf:type schema:Person
    106 Na881fa9459ce4625bca89829480bf00f schema:name doi
    107 schema:value 10.1007/s00202-019-00761-5
    108 rdf:type schema:PropertyValue
    109 Nd4f3079304ea4704a2e03a981d300d18 schema:name readcube_id
    110 schema:value fe6002ccea625fc4b4f3f7dce779ce22261c32cbfeec5bb36382b45229c6502a
    111 rdf:type schema:PropertyValue
    112 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    113 schema:name Information and Computing Sciences
    114 rdf:type schema:DefinedTerm
    115 anzsrc-for:0803 schema:inDefinedTermSet anzsrc-for:
    116 schema:name Computer Software
    117 rdf:type schema:DefinedTerm
    118 sg:journal.1136871 schema:issn 0095-9197
    119 2376-7804
    120 schema:name Electrical Engineering
    121 rdf:type schema:Periodical
    122 sg:pub.10.1007/978-3-319-45474-0_37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084918008
    123 https://doi.org/10.1007/978-3-319-45474-0_37
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/978-3-319-78458-8_13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101676942
    126 https://doi.org/10.1007/978-3-319-78458-8_13
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1007/978-3-540-24844-6_175 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001754970
    129 https://doi.org/10.1007/978-3-540-24844-6_175
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1007/s00034-018-0794-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101373801
    132 https://doi.org/10.1007/s00034-018-0794-8
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1007/s00202-009-0117-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1036815176
    135 https://doi.org/10.1007/s00202-009-0117-y
    136 rdf:type schema:CreativeWork
    137 https://app.dimensions.ai/details/publication/pub.1101409827 schema:CreativeWork
    138 https://app.dimensions.ai/details/publication/pub.1103285047 schema:CreativeWork
    139 https://app.dimensions.ai/details/publication/pub.1107041725 schema:CreativeWork
    140 https://doi.org/10.1002/9781119307181 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107041725
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1002/elps.201600551 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085120097
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1016/0142-0615(95)00045-r schema:sameAs https://app.dimensions.ai/details/publication/pub.1043862670
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1016/j.amc.2014.12.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029722019
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1016/j.amc.2017.01.047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083822818
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1016/j.amc.2017.02.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084057199
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1016/j.amc.2017.06.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086090353
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1016/j.amc.2018.05.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105051438
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1016/j.ijepes.2008.10.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029335956
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1016/j.ijepes.2010.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006685006
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1016/j.ijepes.2011.06.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044619700
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1016/j.ijepes.2014.03.036 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012236762
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1016/j.ijepes.2017.05.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085862916
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1016/j.ijepes.2018.10.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107640013
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1016/j.infrared.2015.06.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004796146
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1016/j.jfranklin.2018.08.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106334160
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1016/j.jmatprotec.2004.07.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015173340
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1016/j.jmmm.2006.03.046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015956086
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1016/j.jmmm.2018.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106087154
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1016/j.matcom.2013.09.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012533294
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1016/s0304-8853(02)01463-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034576837
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1016/s0378-7796(98)00117-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002761328
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1049/ip-c.1993.0035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056843816
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1063/1.4747915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058058429
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1088/0022-3727/41/4/045001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006432910
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1088/1361-6463/aab0e1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101132108
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1090/gsm/140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098735254
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1108/03321641311317130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010370912
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1108/compel-01-2013-0013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002109830
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1109/61.131146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061198924
    199 rdf:type schema:CreativeWork
    200 https://doi.org/10.1109/61.296285 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061199729
    201 rdf:type schema:CreativeWork
    202 https://doi.org/10.1109/61.847260 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061201401
    203 rdf:type schema:CreativeWork
    204 https://doi.org/10.1109/61.905606 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061201668
    205 rdf:type schema:CreativeWork
    206 https://doi.org/10.1109/atee.2017.7905167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094114061
    207 rdf:type schema:CreativeWork
    208 https://doi.org/10.1109/eeeic.2013.6549616 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095194514
    209 rdf:type schema:CreativeWork
    210 https://doi.org/10.1109/i2mtc.2013.6555402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093187998
    211 rdf:type schema:CreativeWork
    212 https://doi.org/10.1109/iiphdw.2018.8388332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105032066
    213 rdf:type schema:CreativeWork
    214 https://doi.org/10.1109/mmar.2018.8485964 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107559184
    215 rdf:type schema:CreativeWork
    216 https://doi.org/10.1109/pes.2003.1270499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094920189
    217 rdf:type schema:CreativeWork
    218 https://doi.org/10.1109/tencon.2008.4766386 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094986504
    219 rdf:type schema:CreativeWork
    220 https://doi.org/10.1109/tmag.2013.2243908 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061685699
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1109/tpwrd.2008.2002668 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061772643
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1109/tpwrd.2014.2320599 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061774649
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1515/aee-2016-0038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050685257
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.15598/aeee.v15i3.2196 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092071187
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.17694/bajece.337936 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091680560
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.17981/ingecuc.11.1.2015.03 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068550986
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.24084/repqj05.317 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090844839
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.24084/repqj10.351 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085353250
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.2478/amm-2014-0094 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044028576
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.2478/bpasts-2013-0059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053591695
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.2478/bpasts-2014-0047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025405603
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.3390/math6020016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100616323
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.3906/elk-1310-129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071567927
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.5923/j.eee.20120202.10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073508717
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.7763/ijcee.2012.v4.606 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074032195
    251 rdf:type schema:CreativeWork
    252 https://www.grid.ac/institutes/grid.6979.1 schema:alternateName Silesian University of Technology
    253 schema:name Silesian University of Technology, Akademicka 10, 44-100, Gliwice, Poland
    254 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...