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Title: A Comparison of Machine Learning Methods for Frequency Nadir Estimation in Power Systems

Conference ·

An increasing penetration level of inverter-based renewable energy resources changes the inertia of power systems, posing challenges for maintaining the desired system frequency stability. An accurate frequency nadir estimation is crucial for power system operators to prepare preventive actions against large frequency excursions. In this paper, five machine learning methods - linear regression, gradient boosting, support vector regression, an artificial neural network, and XGBoost - are applied to two different datasets, i.e., 1) the unit generation dataset and 2) the system total inertia and headroom dataset, for the prediction of the frequency nadir. The training and testing datasets are generated through extensive generation scheduling simulations using Multi-timescale Integrated Dynamic and Scheduling (MI-DAS) toolbox on the Western Electricity Coordinating Council 240-bus system with high renewable penetration levels. Numerical results show that all five machine learning methods perform well in predicting the nadir frequency of the system. Among them, the gradient boosting and the XGBoost are clear winners yielding the best prediction accuracy in terms of four evaluation metrics.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
DOE Contract Number:
AC36-08GO28308
OSTI ID:
1883203
Report Number(s):
NREL/CP-6A40-83783; MainId:84556; UUID:e17c2394-8ea6-4480-82ef-118f4f24ed5d; MainAdminID:65153
Resource Relation:
Conference: Presented at the 2022 IEEE Kansas Power and Energy Conference (KPEC), 25-26 April 2022, Manhattan, Kansas; Related Information: 82116
Country of Publication:
United States
Language:
English

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