AUTHOR=Cai Guowei , Guo Shujia , Liu Cheng 

TITLE=Parameter Identification of Electromechanical Oscillation Mode in Power Systems Driven by Data: A Quasi-Real-Time Method Based on Randomized-DMD-Multilayer Artificial Neural Networks

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.908937

DOI=10.3389/fenrg.2022.908937

ISSN=2296-598X

ABSTRACT=With the increase of power system scale, the identification of electromechanical oscillation mode parameters by traditional numerical methods can no longer meet the requirements of complete real-time analysis. Therefore, a method based on machine learning (multi-layer artificial neural network) is proposed to identify the electromechanical oscillation mode parameters of the power system. This method can take the monitorable variables of WAMS as the input of the model and the key characteristic information such as frequency and damping ratio as the output. After the corresponding processing with Randomized Dynamic Mode Decomposition (Randomized-DMD) of the input and output data, the mapping relationship of them can be analyzed by using the multilayer neuron architecture. The simulation results of the 4-generator 2-area system and the IEEE 16-generator 5-area show that this method can accurately calculate the key characteristic parameters of the system without considering the change of control parameters and after the offline training of historical data, which shown higher accuracy, stronger robustness and sensitive online tracking ability.