| Title | Deep Learning for Model Parameter Calibration in Power Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Wshah, Safwan, Shadid, Reem, Wu, Yuhao, Matar, Mustafa, Xu, Beilei, Wu, Wencheng, Lin, Lei, Elmoudi, Ramadan |
| Conference Name | 2020 IEEE International Conference on Power Systems Technology (POWERCON) |
| Keywords | calibration, convolution neural network, convolutional neural networks, Deep Learning, Logic gates, machine learning, mathematical models, neural network resiliency, Neural networks, Parameter Calibration, Parameter Verification, phasor measurement units, power system reliability, pubcrawl, recurrent neural network, resilience, Resiliency |
| Abstract | In power systems, having accurate device models is crucial for grid reliability, availability, and resiliency. Existing model calibration methods based on mathematical approaches often lead to multiple solutions due to the ill-posed nature of the problem, which would require further interventions from the field engineers in order to select the optimal solution. In this paper, we present a novel deep-learning-based approach for model parameter calibration in power systems. Our study focused on the generator model as an example. We studied several deep-learning-based approaches including 1-D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), which were trained to estimate model parameters using simulated Phasor Measurement Unit (PMU) data. Quantitative evaluations showed that our proposed methods can achieve high accuracy in estimating the model parameters, i.e., achieved a 0.0079 MSE on the testing dataset. We consider these promising results to be the basis for further exploration and development of advanced tools for model validation and calibration. |
| DOI | 10.1109/POWERCON48463.2020.9230531 |
| Citation Key | wshah_deep_2020 |
Deep Learning for Model Parameter Calibration in Power Systems