Short-Term Voltage Stability Assessment of Multi-infeed HVDC Systems Based on JMIM and XGBoost

The intensive infeed of multiple large-capacity HVDCs into the receiving-end power system has introduced a significant challenge on short-term voltage stability (STVS) management. This brings STVS evaluation a necessity to enable enhanced system evolution performance against the risk of blackouts. T...

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Bibliographic Details
Published in2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES) pp. 752 - 758
Main Authors Yu, Linlin, Liu, Wanxun, Si, Ruihua, Xing, Pengxiang, Huang, Mingzeng, Wen, Yunfeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.03.2021
Subjects
Complexity theory
HVDC transmission
JMIM
machine learning
multi-infeed HVDC systems
Power measurement
Power system stability
short-term voltage stability
Simulation
Stability analysis
Velocity measurement
XGBoost
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Summary:The intensive infeed of multiple large-capacity HVDCs into the receiving-end power system has introduced a significant challenge on short-term voltage stability (STVS) management. This brings STVS evaluation a necessity to enable enhanced system evolution performance against the risk of blackouts. To tackle this issue, this paper proposes a fast and accurate STVS assessment approach based on Joint mutual information maximization (JMIM) and eXtreme Gradient Boosting (XGBoost). JMIM efficiently selects crucial input features from the raw features with high dimensions, thereby reducing the complexity of the model and avoiding the dimension explosion issue. Aided by the second-order Tailor expansion and the regularization term, improved STVS assessment performance can be achieved via XGBoost. Simulation results on the modified New England 39-bus system demonstrate the superiority of the proposed approach over some state-of-the-art machine learning algorithms.
DOI:10.1109/AEEES51875.2021.9403146