Online evaluation of power system inertia based on LSTM deep-learning network

Xinyu Cai

doi:10.1117/12.2689541

10 August 2023 Online evaluation of power system inertia based on LSTM deep-learning network

Xinyu Cai

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127482S (2023) https://doi.org/10.1117/12.2689541
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

Conventional algorithms typically rely on system identification techniques to estimate the inertia of power systems online. However, selecting an appropriate model order can be challenging, and an incorrect choice can lead to significant errors. To address this issue, we propose an algorithm based on Long Short-Term Memory Network (LSTM) deep learning networks for power system inertia identification. In our approach, we preprocess and input frequency and power deviation data obtained from monitoring into the LSTM model for learning. Additionally, we utilize the multi-sampling point method to reduce errors introduced by approximation algorithms. Once we obtain the inertia time constant for each unit, we calculate the system's overall inertia. Finally, we build a simulation system using MATLAB/Simulink to demonstrate the effectiveness and accuracy of our proposed method.

Citation Download Citation

Xinyu Cai "Online evaluation of power system inertia based on LSTM deep-learning network", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127482S (10 August 2023); https://doi.org/10.1117/12.2689541

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