@inproceedings{8690e35e01bf452a99b8d588a259bf57,
title = "Power System Inertia Estimation Based on Long Short Term Memory Network",
abstract = "With the increasingly high penetration of renewable energy into the power system, traditional synchronous generators are gradually substituted by power-electronic-converter-based sources that commonly lack inertia support capability. Since deficient system inertia could cause larger frequency deviation under the same disturbance, commanding the inertia level of the system is beneficial and necessary for the operators to take precautions and ensure stable power supply. This paper puts forward a long short-term memory (LSTM) based inertia estimation method. Through massive training, the LSTM network successfully constructs the relationship between the system inertia and the system frequency sequence as well as the unbalanced power. The hyperparameters of the proposed deep learning model can be determined by the classic grid search. Case studies on the IEEE-39 system verify that the LSTM network can well handle the time-varying sequence and the proposed method can accurately estimate the system inertia.",
keywords = "deep learning, Frequency response, inertia estimation, LSTM",
author = "Zhen Tang and Lihua Hao and Jiapeng Li and Qiuyi Chen and Yujun Li and Zhao Xu",
note = "Funding Information: This paper is supported by the Electric Power Research Institute, State Grid Shanxi Electric Power Company under Grand SGTYHT/19-JS-215. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021 ; Conference date: 22-12-2021 Through 24-12-2021",
year = "2021",
doi = "10.1109/iSPEC53008.2021.9736068",
language = "English",
series = "Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2542--2547",
booktitle = "Proceedings - 2021 IEEE Sustainable Power and Energy Conference",
}