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A Parallel Multi-Modal LSTM for Power System Model Calibration

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posted on 2022-02-03, 04:43 authored by Seyyed Rashid khazeiynasabSeyyed Rashid khazeiynasab
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.


Email Address of Submitting Author

Submitting Author's Institution

university of central flroida

Submitting Author's Country

  • United States of America

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