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

Conference · Tue May 10 04:00:00 EDT 2022

OSTI ID:1868052

Liu, Xuebo; Xie, Jian; Fang, Xin; Yuan, Haoyu; Wang, Bin; Wu, Hongyu; Tan, Jin

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 sets of preprocess data for the prediction of the frequency nadir in the Western Electricity Coordinating Council 240-bus system with high renewable penetration levels. The training and testing data sets are collected by extensive generation scheduling simulations on the Multi-timescale Integrated Dynamic and Scheduling (MIDAS) toolbox. Numerical results show that all five machine learning methods can achieve high performance accuracy for power system nadir frequency estimation. Among them, the gradient boosting and the XGBoost are clear winners by providing the best prediction accuracy.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
• DOE Contract Number: AC36-08GO28308; AC36-08GO28308
• OSTI ID: 1868052
• Report Number(s): NREL/CP-6A40-82116; MainId:82889; UUID:c97a3caf-2992-4c4a-acc5-49f9c755d020; MainAdminID:64491
• Country of Publication: United States
• Language: English

Related Subjects

ENERGY PLANNING, POLICY, AND ECONOMY
data driven
frequency nadir
machine learning
power system stability