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Transformer Maintenance Strategies: A K-Means Based Approach for 33 kV DTs |
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| รหัสดีโอไอ | |
| Creator | Kittisak Chaisuwan |
| Title | Transformer Maintenance Strategies: A K-Means Based Approach for 33 kV DTs |
| Contributor | Paradon Boonmeeruk, Kiattisak Wongsopanakul |
| Publisher | Faculty of Engineering Mahasasakham University |
| Publication Year | 2568 |
| Journal Title | Engineering Access |
| Journal Vol. | 11 |
| Journal No. | 2 |
| Page no. | 151-162 |
| Keyword | Distribution transformer (DT) condition assessment, preventive maintenance planning, K-means clustering algorithm, Transformer insulation analysis, Provincial Electricity Authority (PEA) |
| URL Website | https://ph02.tci-thaijo.org/index.php/mijet/index |
| Website title | THAIJO Engineering Access |
| ISSN | 2730-4175 |
| Abstract | The distribution transformer (DT) is crucial for connecting utility providers to consumers, and its failure can disrupt the distribution network's reliability. The Provincial Electricity Authority (PEA) in Thailand manages a large number of transformers, necessitating efficient maintenance planning to prevent DT failures. This paper introduces a method for classifying the condition of 33 kV DTs without pre-existing cluster data, utilizing the K-means clustering algorithm on data from 150 samples. The dataset includes 7 features from DT annual maintenance records and the Geographic Information System (GIS) of PEA Southern Area 3. Key factors identified are insulation between high voltage and ground, high-low voltage, and low voltage-ground. The method categorizes DT conditions into three clusters: "poor," requiring urgent action; "risk," requiring close monitoring; and "normal," requiring routine maintenance. Validation with K-Nearest Neighbors yields an accuracy of 96.67%, demonstrating the effectiveness of the proposed classification method. |
