Predictive Maintenance for Electrical Substation Components Using K-Means Clustering: A Case Study

Abstract

PT PLN (Persero) UP2D Kalselteng aims to provide reliable electricity supply, necessitating effective substation maintenance. This study proposes a predictive maintenance approach using K-means clustering on electrical current performance data from eight components in the Amuntai main electrical substation. The data undergoes preprocessing, including mapping to absolute z-scores to address electricity fluctuations. The K-means algorithm clusters performances, and models are evaluated using Silhouette scores. Results indicate the potential for predicting maintenance needs, as clusters align with real power outage data. The proposed method provides a proactive strategy for substation maintenance, enhancing system reliability. Feature combination experiments reveal that individual models for transformers and feeders are optimal. Hyperparameter tuning refines models, showcasing silhouette scores above 0.5, indicative of high-quality clusters. Comparisons with real-world power outage data validate the model's capability to identify anomalies, reinforcing the feasibility of the predictive maintenance approach. While the study demonstrates promise, on-field implementation and additional experiments are crucial for comprehensive validation and refinement of the predictive maintenance models.