Transformer Maintenance Strategies: A K-Means Based Approach for 33 kV DTs - Chaisuwan, Kittisak; Boonmeeruk, Paradon; Wongsopanakul, Kiattisak (Engineering Access, 2025)
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.