Physics-Informed Neural Networks (PINNs) for Real-Time Grid-Forming Inverter Control: Embedding Physical Laws into Deep Learning Models

The increasing integration of inverter-based renewable energy sources presents new challenges in maintaining grid stability, particularly in weak or islanded microgrids. Traditional inverter control methods such as droop and virtual synchronous machine (VSM) control suffer from limited adaptability and poor transient response under high variability conditions. This paper proposes a novel Physics-Informed Neural Network (PINN) framework for real-time grid-forming inverter control that directly embeds physical laws such as Kirchhoff’s laws, swing equations, and dynamic stability margins into the learning process of a deep neural model. The proposed controller simultaneously learns voltage and frequency reference signals while preserving physical consistency, enabling robust operation under load disturbances and renewable generation variability. Extensive simulations and hardware-in-the-loop (HIL) validations demonstrate that the PINN-based controller significantly outperforms conventional techniques across key metrics, including frequency deviation, voltage regulation, tracking accuracy, harmonic distortion, and computational latency. Notably, the PINN approach achieves a frequency deviation of less than 0.12 Hz and voltage fluctuation under 0.02 pu, while maintaining real-time inference with an average latency below 0.7 ms.These results confirm that embedding domain-specific physical constraints into neural architectures enhances both performance and interpretability, offering a promising pathway for intelligent, real-time inverter control in next-generation microgrids. The framework provides a scalable and reliable solution for supporting high penetration renewable energy systems and accelerating the transition toward decentralized, low-carbon energy futures.