Machine learning framework for predicting ESDD on high-voltage glass insulators with SHAP-based feature optimization.

Abstract

This paper introduces the use of machine learning (ML) models to predict the severity of pollution on high-voltage outdoor glass insulators using equivalent salt deposit density (ESDD). Four ML models, including Support Vector Machines (SVM), Neural Networks (NN), Extremely Randomized Trees (ERT), and Gaussian Process Regression (GPR), were applied to train 362 data points encompassing fifteen environmental and operational features. Hyperparameter optimization is applied using Bayesian optimization (BO) and random search to enhance model performance. Results show that GPR achieves the highest predictive accuracy, followed by SVM, NN, and ERT across all optimization strategies. SHAP (SHapley Additive exPlanations) was used to identify the most influential features on the performance of the used models based on the training dataset. Models using the top 10 features significantly enhance accuracy and reduce complexity compared to models using the top 5 and 15 features, particularly GPR and ERT models. SHAP-based feature selection and hyperparameter optimization led to the development of efficient, accurate, and interpretable models for predicting contamination severity on high-voltage insulators.