Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis
The world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Ef...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10103865/ |
| _version_ | 1797835803658813440 |
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| author | Minh Ly Duc Petr Bilik Radek Martinek |
| author_facet | Minh Ly Duc Petr Bilik Radek Martinek |
| author_sort | Minh Ly Duc |
| collection | DOAJ |
| description | The world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Efficiency (E), Security (S), and Reliability (R) for Electric Power Quality (EPQ). We measure the harmonic mitigation regulations listed in the IEEE 519 standard. To evaluate the proposed E, S, and R constructs and their relationship to EPQ, a multi-planning approach the method of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep Learning Artificial Neural Network (ANN) analysis were performed. In it, deep Learning Artificial Neural Network (ANN) was performed to complement the PLS-SEM findings and higher prediction accuracy. The study shows that the aspects of efficiency (E), security (S), and reliability (R) have a significant relationship with Electric Power Quality (EPQ). Another result of the study indicates that science, technology, engineering and math (STEM) resource conditions have a significant and positive impact on EPQ. |
| first_indexed | 2024-04-09T14:58:19Z |
| format | Article |
| id | doaj.art-2f04535afa09427dbe9df3b82e796d77 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T14:58:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2f04535afa09427dbe9df3b82e796d772023-05-01T23:01:26ZengIEEEIEEE Access2169-35362023-01-0111405914060710.1109/ACCESS.2023.326803710103865Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network AnalysisMinh Ly Duc0https://orcid.org/0000-0003-0200-7153Petr Bilik1https://orcid.org/0000-0001-8655-778XRadek Martinek2https://orcid.org/0000-0003-2054-143XFaculty of Commerce, Van Lang University, Ho Chi Minh City, Binh Thanh, VietnamDepartment of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, Ostrava, Czech RepublicDepartment of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, Ostrava, Czech RepublicThe world today is increasingly dependent directly or indirectly on the power system. Ensuring the quality of power supplied to electrical equipment is essential. The national regulatory framework is for harmonic mitigation in the global power system. This paper discusses the relationship between Efficiency (E), Security (S), and Reliability (R) for Electric Power Quality (EPQ). We measure the harmonic mitigation regulations listed in the IEEE 519 standard. To evaluate the proposed E, S, and R constructs and their relationship to EPQ, a multi-planning approach the method of Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Deep Learning Artificial Neural Network (ANN) analysis were performed. In it, deep Learning Artificial Neural Network (ANN) was performed to complement the PLS-SEM findings and higher prediction accuracy. The study shows that the aspects of efficiency (E), security (S), and reliability (R) have a significant relationship with Electric Power Quality (EPQ). Another result of the study indicates that science, technology, engineering and math (STEM) resource conditions have a significant and positive impact on EPQ.https://ieeexplore.ieee.org/document/10103865/Harmonic mitigationpartial least squares- structural equation modelingPLS-SEMartificial neural network (ANN)electric power quality |
| spellingShingle | Minh Ly Duc Petr Bilik Radek Martinek Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis IEEE Access Harmonic mitigation partial least squares- structural equation modeling PLS-SEM artificial neural network (ANN) electric power quality |
| title | Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis |
| title_full | Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis |
| title_fullStr | Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis |
| title_full_unstemmed | Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis |
| title_short | Analysis of Factors Affecting Electric Power Quality: PLS-SEM and Deep Learning Neural Network Analysis |
| title_sort | analysis of factors affecting electric power quality pls sem and deep learning neural network analysis |
| topic | Harmonic mitigation partial least squares- structural equation modeling PLS-SEM artificial neural network (ANN) electric power quality |
| url | https://ieeexplore.ieee.org/document/10103865/ |
| work_keys_str_mv | AT minhlyduc analysisoffactorsaffectingelectricpowerqualityplssemanddeeplearningneuralnetworkanalysis AT petrbilik analysisoffactorsaffectingelectricpowerqualityplssemanddeeplearningneuralnetworkanalysis AT radekmartinek analysisoffactorsaffectingelectricpowerqualityplssemanddeeplearningneuralnetworkanalysis |