Learning a Generator Model from Terminal Bus Data

Nikolay Stulov; Dejan J Sobajic; Yury Maximov; Deepjyoti Deka; Michael Chertkov

doi:10.1016/j.epsr.2020.106742

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arXiv · 1901.00781

Learning a Generator Model from Terminal Bus Data

Nikolay Stulov·Dejan J Sobajic·Yury Maximov·Deepjyoti Deka·Michael Chertkov

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Abstract

In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques. The goal is to develop an emulator which is trained online and is capable of fast predictive computations. The training is illustrated on synthetic data generated based on available open-source dynamical generator model. Two ML techniques were developed and tested: (a) standard vector auto-regressive (VAR) model; and (b) novel customized long short-term memory (LSTM) deep learning model. Trade-offs in reconstruction ability between computationally light but linear AR model and powerful but computationally demanding LSTM model are established and analyzed.

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BibTeX RIS

Nikolay Stulov, Dejan J Sobajic, Yury Maximov, Deepjyoti Deka, Michael Chertkov. 2019-01-03. Learning a Generator Model from Terminal Bus Data. https://doi.org/10.1016/j.epsr.2020.106742

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