Ambient Synchrophasor Measurement Based System Inertia Estimation
Paper
Paper/Presentation Title | Ambient Synchrophasor Measurement Based System Inertia Estimation |
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Presentation Type | Paper |
Authors | Cui, Yi (Author), You, Shutang (Author) and Liu, Yilu (Author) |
Journal or Proceedings Title | Proceedings of the 2020 IEEE Power and Energy Society General Meeting (PESGM) |
ERA Conference ID | 50486 |
Number of Pages | 5 |
Year | 2020 |
Place of Publication | Montreal, Canada |
ISBN | 9781728155081 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/pesgm41954.2020.9281662 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/9281662 |
Conference/Event | 2020 IEEE Power and Energy Society General Meeting (PESGM) |
IEEE Power and Energy Society General Meeting | |
Event Details | 2020 IEEE Power and Energy Society General Meeting (PESGM) Event Date 02 to end of 06 Aug 2020 Event Location Montreal, Canada |
Event Details | IEEE Power and Energy Society General Meeting PES-GM |
Abstract | This paper develops an algorithm to estimate the system inertia value based on ambient synchrophasor measurement. Informative features are extracted from ambient synchrophasor measurements for machine-learning-based inertia estimation. Besides ambient synchrophasor measurements of FNET/GridEye, other available data relevant to inertia (such as weather and system load data) are also used to improve the inertia estimation accuracy. Then a machine learning algorithm to estimate system inertia is developed. A test dataset including ambient synchrophasor data from FNET/GridEye measurements and the WECC system inertia data from NERC is used to evaluate the performance of the developed inertia estimation method. The average and maximum estimation errors of the developed inertia estimation method is lower than 5% and 10%, respectively. This accuracy is higher than reported accuracy values in existing literature. |
Keywords | inertia estimation, ambient synchrophasor data, random forest |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Tennessee, United States |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w0x/ambient-synchrophasor-measurement-based-system-inertia-estimation
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