A Method for Estimating the Inertia of Interconnected New Energy Power Systems Based on KAN-Transformer

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A Method for Estimating the Inertia of Interconnected New Energy Power Systems Based on KAN-Transformer

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[1]ZHANG Jianhua,CHENG Xiaoxuan,HUANG Dehao.A Method for Estimating the Inertia of Interconnected New Energy Power Systems Based on KAN-Transformer[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):143-150.[doi:10.13705/j.issn.1671-6833.2026.03.004]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 3 Page number: 143-150 Column: Public date: 2026-05-27

Title:: A Method for Estimating the Inertia of Interconnected New Energy Power Systems Based on KAN-Transformer

Author(s):: ZHANG Jianhua^1,2, CHENG Xiaoxuan¹, HUANG Dehao¹; 1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;2.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Beijing 102206, China

Keywords:: interconnected inertia estimation; low-inertia; deep learning network; Transformer; KAN; inertia distribution visualization

CLC:: TP29:TM715

DOI:: 10.13705/j.issn.1671-6833.2026.03.004

Abstract:: Aiming at the problem of inertia estimation of regional interconnected power systems with a high proportion of renewable energy connected to the grid, a new estimation algorithm named kernel-Attention-Transformer based on the combination of Transformer and kernel attention network was proposed to accurately and efficiently estimate the regional inertia of the system. Then, the self-attention mechanism of Transformer was used to extract the dynamic features of the system, and KAN’s kernel attention mechanism was combined to replace the traditional fully connected layer and softmax layer, which enhanced the adaptability and robustness of the model for complex nonlinear and strong random data. In addition, through the visualization of inertia distribution, real-time monitoring of inertia changes was realized, which provided intuitive decision-making basis for system operators. Verified in the improved Australian 14-machine 59-node system, compared with the traditional RNN, LSTM, GRU and Transformer, the proposed kernel attention-Transformer algorithm significantly improved the noise filtering ability and estimation accuracy under different simulation backgrounds. At the same time, the visualization results of inertia distribution clearly showed the spatio-temporal variation characteristics of the system inertia, which provided strong support for the safe and stable operation of the power system.

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Last Update: 2026-05-27