)
Hybrid Intelligence for the Grid: Merging Physics-Based and Machine Learning Models
Electric utilities are increasingly using models to understand the behavior, condition, and failure risk of critical overhead components such poles, towers, and conductors subjected to environmental loading. Physics-based models offer interpretability and consistency with known mechanisms such as thermal aging, insulation degradation, and mechanical stress, but they can be difficult to calibrate and computationally expensive at scale. Machine learning models can uncover complex patterns in large streams of operational and sensor data and often perform well for prediction, anomaly detection, and asset health assessment, but they may be limited by data quality, limited extrapolation, and reduced transparency. This panel will examine the strengths and tradeoffs of both approaches, with particular emphasis on where each is most useful in utility practice. It will also explore emerging hybrid methods that combine first-principles knowledge with data-driven learning to improve accuracy, robustness, and trustworthiness. Panelists will discuss practical use cases, data and validation challenges, and how combined modeling strategies can support maintenance planning, reliability, and grid modernization.
