A Residual Physics-Informed Neural Network Approach for Identifying Dynamic Parameters in Swing Equation-Based Power Systems

Jiani Zeng, Xianglong Li, Hanqi Dai, Lu Zhang, Weixian Wang, Zihan Zhang (), Shengxin Kong and Liwen Xu ()
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Jiani Zeng: Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China
Xianglong Li: Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China
Hanqi Dai: Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China
Lu Zhang: Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China
Weixian Wang: Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China
Zihan Zhang: College of Science, North China University of Technology, Beijing 100144, China
Shengxin Kong: College of Science, North China University of Technology, Beijing 100144, China
Liwen Xu: College of Science, North China University of Technology, Beijing 100144, China

Energies, 2025, vol. 18, issue 11, 1-16

Abstract: Several challenges hinder accurate and physically consistent dynamic parameter estimation in power systems, particularly under scenarios involving limited measurements, strong system nonlinearity, and high variability introduced by renewable integration. Although data-driven methods such as Physics-Informed Neural Networks (PINNs) provide a promising direction, they often suffer from poor generalization and training instability when faced with complex dynamic regimes. To address these challenges, we propose a Residual Physics-Informed Neural Network (Res-PINN) framework, which integrates a residual neural architecture with the swing equation to enhance estimation robustness and precision. By replacing the traditional multilayer perceptron (MLP) in PINN with residual connections and injecting normalized time into each network layer, the proposed model improves temporal awareness and enables stable training of deep networks. A physics-constrained loss formulation is employed to estimate inertia and damping parameters without relying on large-scale labeled datasets. Extensive experiments on a 4-bus, 2-generator power system demonstrate that Res-PINN achieves high parameter estimation accuracy across various dynamic conditions and outperforms traditional PINN and Unscented Kalman Filter (UKF) methods. It also exhibits strong robustness to noise and low sensitivity to hyperparameter variations. These results show the potential of Res-PINN to bridge the gap between physics-guided learning and practical power system modeling and parameter identification.

Keywords: physics-informed neural networks; residual neural networks; inverse problems; nonlinear differential equations; dynamic parameter estimation; swing equation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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