Renewable energies, smart loads, energy storage, and new market behavior are adding new sources of uncertainty to power systems. Therefore, planning in real-time and developing high-quality models is crucial to adapt to uncertainties. Model validation based on actual measurements is necessary for obtaining accurate representations of power systems dynamics with system uncertainties. This paper presents a new measurement-based method to calibrate the parameters of a synchronous generator by deep learning method based on the long-short term memory (LSTM) network. First, critical parameters are determined regarding the active/reactive behavior of the generator. Then, a parallel multimodal LSTM (PM-LSTM) is designed with flexible input time steps to capture important features of temporal patterns from time-series measurements. The extracted features are then fed into a dense layer to capture the joint representation of inputs. The simulations conducted for a hydro generator under different events show that the proposed method can estimate the model parameters accurately and efficiently.
