Abstract
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.
Original language | American English |
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Number of pages | 8 |
State | Published - 2022 |
Event | IEEE Kansas Power & Energy Conference - Manhattan, Kansas Duration: 25 Apr 2022 → 26 Apr 2022 |
Conference
Conference | IEEE Kansas Power & Energy Conference |
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City | Manhattan, Kansas |
Period | 25/04/22 → 26/04/22 |
Bibliographical note
See NREL/CP-6A40-83783 for paper as published in proceedingsNREL Publication Number
- NREL/CP-6A40-82116
Keywords
- data driven
- frequency nadir
- machine learning
- power system stability