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

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.