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
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.