Deep Learning for Model Parameter Calibration in Power Systems
| Autor: | Beilei Xu, Reem Shadid, Ramadan Elmoudi, Yuhao Wu, Safwan Wshah, Mustafa Matar, Lei Lin, Wencheng Wu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Engineering
business.industry Calibration (statistics) 020209 energy Reliability (computer networking) Deep learning 020208 electrical & electronic engineering 02 engineering and technology Grid computer.software_genre Phasor measurement unit Convolutional neural network Electric power system Recurrent neural network 0202 electrical engineering electronic engineering information engineering Artificial intelligence Data mining business computer |
| Zdroj: | 2020 IEEE International Conference on Power Systems Technology (POWERCON). |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|