# Learning a Generator Model from Terminal Bus Data

## TL;DR

This paper explores machine learning methods, including VAR and LSTM models, to reconstruct generator models from terminal bus data, aiming for fast, online predictive emulation of power generators.

## Contribution

It introduces a novel LSTM-based approach for generator model reconstruction and compares it with traditional VAR models in terms of performance and computational efficiency.

## Key findings

- LSTM outperforms VAR in predictive accuracy.
- VAR is more computationally efficient.
- Trade-offs identified between model complexity and performance.

## 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.