skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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:
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

References (18)

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors journal December 2021
Hourly occupant clothing decisions in residential HVAC energy management journal August 2021
Machine learning approaches to the unit commitment problem: Current trends, emerging challenges, and new strategies journal January 2021
Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources journal October 2021
A Review of Machine Learning Applications in Power System Resilience conference August 2020
An Analytical Model for Frequency Nadir Prediction Following a Major Disturbance journal July 2020
Nadir Frequency Estimation in Low-Inertia Power Systems conference June 2020
Developing a Reduced 240-Bus WECC Dynamic Model for Frequency Response Study of High Renewable Integration conference October 2020
Coordinated operation of water and electricity distribution networks with variable renewable energy and distribution locational marginal pricing journal November 2021
Distribution locational marginal price-based transactive day-ahead market with variable renewable generation journal February 2020
Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time journal November 2019
A Fast Penalty-Based Gauss-Seidel Method for Stochastic Unit Commitment With Uncertain Load and Wind Generation journal January 2021
An Adaptive PV Frequency Control Strategy Based on Real-Time Inertia Estimation journal May 2021
Synthetic inertia versus fast frequency response: a definition journal April 2018
Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM journal June 2020
machine. journal October 2001
Support Vector Regression book April 2015
XGBoost: A Scalable Tree Boosting System conference January 2016