🤖 AI Summary
This study addresses key challenges in power system modeling—namely data scarcity, poor interpretability, and the imperative to strictly adhere to physical laws—by systematically reviewing and comparing hybrid architectures that integrate physical priors with machine learning. These include physics-informed neural networks (PINNs), DeepONets, Fourier neural operators, and their enhanced variants incorporating graph structures, domain decomposition, and extreme learning machines. By embedding fundamental physical laws such as Maxwell’s equations directly into neural network training, this paradigm substantially improves prediction accuracy and generalization under sparse, noisy data conditions. It achieves simulation speeds orders of magnitude faster than finite element methods and outperforms purely data-driven approaches in dynamic modeling, parameter sensitivity analysis, and real-time digital twin calibration, thereby advancing power system intelligence from opaque black-box models toward transparent, physically interpretable frameworks.
📝 Abstract
The integration of machine learning with domain-specific physics is transforming the design, monitoring, and control of electricity systems, where data scarcity, limited interpretability, and the need to enforce physical laws constrain purely data-driven models. Physics-informed machine learning (PIML) addresses these limitations by embedding governing equations directly into the learning process, yielding accurate, efficient, and scalable solutions for Industry 4.0 applications. This article reviews hybrid PIML architectures for electricity systems, including physics-informed neural networks (PINNs), Deep Operator Networks (DeepONets), Fourier Neural Operators, Extreme Learning Machine-enhanced PINNs, graph-based PINNs (PIGNNs), and domain-decomposition PINNs. Each approach is examined through case studies spanning field analysis, fault detection, digital twins, surrogate modeling, and control optimization. The review shows that embedding Maxwell's equations and other first-principles constraints substantially improves predictive accuracy under sparse and noisy data, reduces simulation time by orders of magnitude relative to finite element methods, and enhances generalization across operating regimes. Hybrid frameworks consistently outperform purely data-driven baselines on parameter sensitivity, dynamic behavior, and robustness, while supporting real-time digital-twin calibration and uncertainty quantification. Persistent challenges include training instability for stiff multi-scale problems, computational cost of high-fidelity models, and the absence of standardized benchmarks. The findings demonstrate that PIML enables a paradigm shift from black-box data-driven methods to transparent, physics-informed strategies, positioning the field for sustained innovation in resilient and intelligent electricity systems.