Machine Learning-Enhanced Hyperspectral Imaging for Real-Time Impurity Monitoring in Smart Grid Liquids

Abstract

A hyperspectral imaging system enhanced by machine learning is developed to monitor impurities in liquid dielectrics of power equipment in real time. This system integrates advanced hyperspectral imaging with machine learning techniques to address the critical challenge of detecting impurities in transformer oils and coolants. Research focuses on developing a field-deployable solution for identifying metallic particles (Fe, Cu) and carbon-based contaminants that degrade dielectric performance in smart grids. The system architecture combines a custom-designed hyperspectral imaging module (390–1010 nm range, 2.5 nm resolution) with a hierarchical ensemble architecture integrating a 1D CNN (5-layer), SVM with RBF kernel, XGBoost, LightGBM, and a GRU meta-learner, optimized via Bayesian tuning and enhanced by preprocessing steps like first-derivative analysis and continuum removal. The experimental results demonstrate robust detection performance across different dielectric liquids, with the system achieving 87.79% accuracy for coolant impurity identification and an higher 91.17% accuracy for transformer oil contaminant detection. The technology establishes a comprehensive spectral fingerprint database specifically for smart grid liquid dielectrics, enabling precise material characterization through unique spectral signatures. The study makes significant contributions to smart grid maintenance by: developing the first real-time, non-invasive monitoring solution for liquid dielectrics; creating an adaptive machine learning framework optimized for spectral analysis; and demonstrating practical implementation in smart grid environments. The system’s modular design and edge-computing compatibility facilitate broader applications in energy infrastructure monitoring.