面向GIS外壳振动系统多参数反演的强化学习驱动物理信息神经网络框架

doi:10.19595/j.cnki.1000-6753.tces.251306

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Abstract To enhance the condition monitoring and diagnostic transparency of power equipment, methods based on inverse problem theory are increasingly important. For gas-insulated switchgear (GIS), where mechanical faults account for a significant portion of failures, parameter inversion of vibration systems is a key step toward interpretable fault diagnosis. However, existing methods, including Physics-Informed Neural Networks (PINNs), face significant challenges in this domain. Specifically, they struggle to accurately reconstruct high-frequency dynamic responses and to effectively coordinate the convergence of multiple, often conflicting, physical loss functions during training. This difficulty arises because the fixed or heuristically adjusted weights used in conventional PINNs are often insufficient to balance loss terms with different physical scales and convergence rates, leading to training instability and suboptimal accuracy. This study aims to address these limitations by developing a new framework that enables adaptive, intelligent control over the PINN training process for robust and accurate multi-parameter inversion in GIS vibration systems.
A reinforcement learning-enhanced PINN (RL-PINN) framework with a nested dual-network architecture is proposed. The inner network is a PINN that models the GIS shell's vibration dynamics using a 3D continuous medium partial differential equation (PDE), guided by a composite loss function. The outer network is a reinforcement learning (RL) agent, structured as an Actor-Critic model, which dynamically adjusts the weights of each term in the PINN's loss function. The RL agent observes the PINN's training state—including loss values and high-frequency residuals—and selects weights to maximize a long-term cumulative reward that balances convergence and accuracy. This transforms the manual weight-tuning problem into a solved sequential decision-making task. The framework was validated using a dedicated experimental platform with a simplified GIS shell. A 100 Hz harmonic excitation was applied to simulate dominant operational vibrations, and response data was collected using a 12-accelerometer array under various spatial configurations to test for robustness.
The RL-PINN framework demonstrated superior performance compared to a conventional deep neural network (DNN) and a DWA-PINN (a PINN using a dynamic weight averaging method). In parameter inversion, RL-PINN achieved the lowest relative errors for equivalent damping (3.71%) and stiffness (2.11%), representing a maximum error reduction of 47.0% and 57.1%, respectively. For response reconstruction, it yielded a linear regression R² value of 99.02%, compared to 92.53% for DWA-PINN and 89.12% for DNN. The framework also showed higher robustness to noise, maintaining an error of only 4.61% at a 5% noise level, significantly outperforming the comparative methods.
This study validates that the RL-PINN framework effectively overcomes key challenges in PINN-based parameter inversion by employing an RL agent for intelligent, dynamic loss weighting. This approach yields more accurate and stable results, providing a robust inverse modeling tool essential for developing physics-based fault diagnosis systems for power equipment. Future work will focus on extending the framework to complex, multi-component GIS models, advancing from parameter identification to precise fault source localization, and exploring its use in optimizing sensor placement strategies.

Key words： Reinforcement learning physics-informed neural networks (PINN) gas-insulated switchgear (GIS) parameter inversion vibration system modeling state transparency

Received: 23 July 2025

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	Li Yuhang
	Bai Jin
	Yan Yingjie
	Liu Yadong
	Jiang Xiuchen

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Li Yuhang,Bai Jin,Yan Yingjie等. A Reinforcement Learning-Driven Physics-Informed Neural Network Framework for Multi-Parameter Inversion in the Enclosure Vibration System of Gas-Insulated Switchgear[J]. Transactions of China Electrotechnical Society, 2026, 41(15): 5249-5264.

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