Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier
Industrial production has evolved significantly over the last decade. For this reason, it is necessary to obtain mathematical and computational tools that enable power systems engineers to make decisions that reduce harmonic distortions in accordance with international standards. This paper presents...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8120150/ |
| _version_ | 1818619176842952704 |
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| author | Edson Farias de Oliveira Maria Emilia de Lima Tostes Carlos Alberto Oliveira de Freitas Jandecy Cabral Leite |
| author_facet | Edson Farias de Oliveira Maria Emilia de Lima Tostes Carlos Alberto Oliveira de Freitas Jandecy Cabral Leite |
| author_sort | Edson Farias de Oliveira |
| collection | DOAJ |
| description | Industrial production has evolved significantly over the last decade. For this reason, it is necessary to obtain mathematical and computational tools that enable power systems engineers to make decisions that reduce harmonic distortions in accordance with international standards. This paper presents a total harmonic distortion (THD) assessment based on full knowledge discovery in databases (KDD) using power quality (PQ) standards and computational intelligence tools. The materials and methods of THD assessment consist of load and layout analysis; choice and installation of PQ analyzers; and the application of the full KDD process, including collection, selection, cleaning, integration, transformation and reduction, mining, interpretation, and evaluation of the data. This research methodology was used in an electrical and electronic industry; the results obtained have characteristics that can be used as a reference for other types of analyses. The results indicate that these methods can be applied to several industrial applications such as: 1) the description of the complete KDD process for THD assessment of the point of common coupling; 2) simultaneous collection using five PQ analyzers at several points in the electrical network; and (3) the use of a decision tree classifier. |
| first_indexed | 2024-12-16T17:33:20Z |
| format | Article |
| id | doaj.art-79ef17c2329c45149930f6bc7bb1e4cc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:33:20Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-79ef17c2329c45149930f6bc7bb1e4cc2022-12-21T22:22:52ZengIEEEIEEE Access2169-35362018-01-0161177118810.1109/ACCESS.2017.27780288120150Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree ClassifierEdson Farias de Oliveira0https://orcid.org/0000-0002-1712-7740Maria Emilia de Lima Tostes1Carlos Alberto Oliveira de Freitas2https://orcid.org/0000-0002-8913-953XJandecy Cabral Leite3Post-Graduate Program in Electrical Engineering, Federal University of Para-UFPA, Belem, BrazilPost-Graduate Program in Electrical Engineering, Federal University of Para-UFPA, Belem, BrazilPost-Graduate Program in Electrical Engineering, Federal University of Para-UFPA, Belem, BrazilInstitute of Amazonian Galileo Technology, Manaus, BrazilIndustrial production has evolved significantly over the last decade. For this reason, it is necessary to obtain mathematical and computational tools that enable power systems engineers to make decisions that reduce harmonic distortions in accordance with international standards. This paper presents a total harmonic distortion (THD) assessment based on full knowledge discovery in databases (KDD) using power quality (PQ) standards and computational intelligence tools. The materials and methods of THD assessment consist of load and layout analysis; choice and installation of PQ analyzers; and the application of the full KDD process, including collection, selection, cleaning, integration, transformation and reduction, mining, interpretation, and evaluation of the data. This research methodology was used in an electrical and electronic industry; the results obtained have characteristics that can be used as a reference for other types of analyses. The results indicate that these methods can be applied to several industrial applications such as: 1) the description of the complete KDD process for THD assessment of the point of common coupling; 2) simultaneous collection using five PQ analyzers at several points in the electrical network; and (3) the use of a decision tree classifier.https://ieeexplore.ieee.org/document/8120150/Harmonic distortiondata miningKDDcomputational intelligencedecision treepower quality |
| spellingShingle | Edson Farias de Oliveira Maria Emilia de Lima Tostes Carlos Alberto Oliveira de Freitas Jandecy Cabral Leite Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier IEEE Access Harmonic distortion data mining KDD computational intelligence decision tree power quality |
| title | Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier |
| title_full | Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier |
| title_fullStr | Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier |
| title_full_unstemmed | Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier |
| title_short | Voltage THD Analysis Using Knowledge Discovery in Databases With a Decision Tree Classifier |
| title_sort | voltage thd analysis using knowledge discovery in databases with a decision tree classifier |
| topic | Harmonic distortion data mining KDD computational intelligence decision tree power quality |
| url | https://ieeexplore.ieee.org/document/8120150/ |
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