Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations
In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, laten...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/3878 |
| _version_ | 1797437037281804288 |
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| author | Nayli Adriana Azhar Nurul Asyikin Mohamed Radzi Kaiyisah Hanis Mohd Azmi Faris Syahmi Samidi Alisadikin Muhammad Zainal |
| author_facet | Nayli Adriana Azhar Nurul Asyikin Mohamed Radzi Kaiyisah Hanis Mohd Azmi Faris Syahmi Samidi Alisadikin Muhammad Zainal |
| author_sort | Nayli Adriana Azhar |
| collection | DOAJ |
| description | In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study. |
| first_indexed | 2024-03-09T11:12:12Z |
| format | Article |
| id | doaj.art-97ef99ac999e41c8972daa1697b885ea |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:12:12Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-97ef99ac999e41c8972daa1697b885ea2023-12-01T00:40:41ZengMDPI AGApplied Sciences2076-34172022-04-01128387810.3390/app12083878Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution SubstationsNayli Adriana Azhar0Nurul Asyikin Mohamed Radzi1Kaiyisah Hanis Mohd Azmi2Faris Syahmi Samidi3Alisadikin Muhammad Zainal4UNITEN R & D Sdn. Bhd. (URND), Universiti Tenaga Nasional, Kajang 43000, MalaysiaElectrical and Electronics Engineering Department, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaUNITEN R & D Sdn. Bhd. (URND), Universiti Tenaga Nasional, Kajang 43000, MalaysiaUNITEN R & D Sdn. Bhd. (URND), Universiti Tenaga Nasional, Kajang 43000, MalaysiaAsset Strategy and Policy, Asset Management, Distribution Network Division, Tenaga Nasional Berhad, Kuala Lumpur 56000, MalaysiaIn the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study.https://www.mdpi.com/2076-3417/12/8/3878criteria selectionmachine learningcommunication technologieselectrical distribution substationnaïve bayesdecision tree |
| spellingShingle | Nayli Adriana Azhar Nurul Asyikin Mohamed Radzi Kaiyisah Hanis Mohd Azmi Faris Syahmi Samidi Alisadikin Muhammad Zainal Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations Applied Sciences criteria selection machine learning communication technologies electrical distribution substation naïve bayes decision tree |
| title | Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations |
| title_full | Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations |
| title_fullStr | Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations |
| title_full_unstemmed | Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations |
| title_short | Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations |
| title_sort | criteria selection using machine learning ml for communication technology solution of electrical distribution substations |
| topic | criteria selection machine learning communication technologies electrical distribution substation naïve bayes decision tree |
| url | https://www.mdpi.com/2076-3417/12/8/3878 |
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