Estimation Method of Inertial Time Constant in Power System Based on Improved Long Short-Term Memory Network Model

doi:10.3969/j.issn.1008-0198.2026.05.004

Abstract

Abstract: With the continuous integration of renewable energy, the inertia time constant of the power system has decreased, and accurate estimation of the inertia time constant is becoming increasingly important for the stable and safe operation of the power grid. This article proposes a method for estimating the inertia time constant of power systems by combining the carnivorous plant algorithm (CPA) and long short-term memory network (LSTM) model. The CPA is used to improve the LSTM model and achieve automatic tuning of model hyperparameters, resulting in higher optimization accuracy and stronger global search capability. Finally, the standard IEEE 10-machine 39-bus test system is estabished for verification. Compared with four conventional LSTM-based model inertia time constant estimation methods, the method proposed in this paper has higher estimation accuracy and faster convergence speed.

Key words: new power system, inertia time constant estimation, long short-term memory network model, carnivorous plant algorithm

CLC Number:

TM712

ZHENG Jin, ZHANG Dengji, LI Yibo, LIAN Chenxi, YANG Ling. Estimation Method of Inertial Time Constant in Power System Based on Improved Long Short-Term Memory Network Model[J]. Hunan Electric Power, 2026, 46(5): 24-32.

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