基于深度特征提取与物理可解释回归的新型电力系统节点惯量估计方法

doi:10.19595/j.cnki.1000-6753.tces.260237

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Abstract

Accurate estimation of node-level inertia is essential for maintaining frequency stability in power systems with high penetration of renewable energy sources. The increasing replacement of synchronous generators by power electronic converters leads to reduced system inertia and more pronounced spatial variability, which increases the difficulty of inertia estimation and requires models with both high accuracy and physical interpretability. To address these challenges, a node-level inertia estimation framework combining deep feature extraction and interpretable regression is developed.
The proposed framework consists of a feature extraction module and an interpretable regression module. First, frequency disturbance responses are represented by Rate of Change of Frequency (RoCoF) sequences, which serve as the input to the estimation model. A Residual Convolutional Neural Network (ResNet) is employed to extract multi-scale dynamic features from the RoCoF sequences. Residual connections are introduced to alleviate feature degradation during deep network propagation, thereby preserving critical transient information and improving feature representation capability. Then, the extracted feature vectors are fed into a Fourier Kolmogorov-Arnold Network (FKAN). Compared with conventional multilayer perceptrons, the FKAN constructs explicit nonlinear functional mappings between input features and inertia values. The regression process is expressed through interpretable basis functions while retaining strong nonlinear approximation capability. Finally, the integration of deep feature extraction and interpretable regression enables accurate estimation of node-level inertia while maintaining model transparency.
The effectiveness of the proposed method is verified using the modified IEEE 39-bus system and the IEEE 118-bus system. Ablation studies, noise robustness tests, comparative experiments, and interpretability analyses are conducted on the modified IEEE 39-bus system to evaluate the contribution of each model component and the estimation performance under different operating conditions. The IEEE 118-bus system is used to assess the generalization capability of the trained model in a larger and more complex network.
The results show that the proposed method achieves accurate node-level inertia estimation over a wide range of operating conditions. The residual feature extraction strategy effectively preserves transient dynamic information, while the FKAN establishes explicit nonlinear relationships between features and inertia. Stable estimation performance is maintained under noisy measurement conditions, and consistent results are obtained across different system scales.
The proposed framework combines the representation capability of deep neural networks with the interpretability of explicit functional regression, providing a data-driven approach for node-level inertia estimation in renewable-dominated power systems. The results support its applicability in frequency stability assessment and inertia monitoring.

Key words： novel power system node-level inertia estimation feature extraction interpretability Residual Convolutional Neural Network (ResNet) Fourier Kolmogorov-Arnold Network (FKAN)

Received: 08 February 2026

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TM712

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	Yang Ling
	Li Yibo
	Xu Zhaoyang
	Gong Zhongliang
	Xu Fangyuan

Cite this article:

Yang Ling,Li Yibo,Xu Zhaoyang等. Node-Level Inertia Estimation for Novel Power Systems Based on Deep Feature Extraction and Physically Interpretable Regression[J]. Transactions of China Electrotechnical Society, 0, (): 260237-.

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