This paper presents a novel parameter calibration approach for power system stability models using automatic data generation and advanced deep learning technology. A PMU-measurement-based event playback approach is used to identify potential inaccurate parameters and automatically generate extensive simulation data, which are used for training a convolutional neural network (CNN). The accurate parameters will be predicted by the well-trained CNN model and validated by original PMU measurements. The accuracy and effectiveness of the proposed deep learning approach have been validated through extensive simulation and field data.
@article{arxiv.1905.03172,
title = {Parameters Calibration for Power Grid Stability Models using Deep Learning Methods},
author = {Renke Huang and Rui Fan and Tianzhixi Yin and Shaobu Wang and Zhenyu Tan},
journal= {arXiv preprint arXiv:1905.03172},
year = {2019}
}