期刊
APPLIED SCIENCES-BASEL
卷 8, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/app8091442
关键词
power system security; static security assessment; contingency ranking; contingency screening; machine learning; least absolute shrinkage and selection operator (Lasso); smart grid
类别
资金
- China Scholarship Council (CSC) [201608220144]
- National Natural Science Foundation of China [51677023]
As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton-Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.
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