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arXiv · 1905.03172

Parameters Calibration for Power Grid Stability Models using Deep Learning Methods

Renke Huang·Rui Fan·Tianzhixi Yin·Shaobu Wang·Zhenyu Tan
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Abstract

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.

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Renke Huang, Rui Fan, Tianzhixi Yin, Shaobu Wang, Zhenyu Tan. 2019-05-08. Parameters Calibration for Power Grid Stability Models using Deep Learning Methods. https://arxiv.org/abs/1905.03172

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