Estimating TTC for wind farm integrated power systems based on nonparametric regression analytics

Assessing security margin of operation is vital in situation awareness for power systems with centralized wind farm integration. Total transfer capability (TTC) can be used as an indicator referring to the maximal loadability of transmission corridors. However, the calculation of TTC considering win...

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Bibliographic Details
Main Authors: Youbo Liu, Tingjian Liu, Yue Xiang, Jing Gou, Junyong Liu
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Correlation
group Lasso
Load modeling
Mathematical model
nonparametric modeling
Power systems
scenarios clustering
Total transfer capability
Wind farms
wind power
Wind power generation
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Summary:Assessing security margin of operation is vital in situation awareness for power systems with centralized wind farm integration. Total transfer capability (TTC) can be used as an indicator referring to the maximal loadability of transmission corridors. However, the calculation of TTC considering wind generation is a computation-intensive procedure. In order to assess TTC, an estimation approach based on nonparametric regression analytics is presented which takes into account clustering, feature confirmation, samples generation and group Lasso regression modeling. The estimated result is comparable to the performance of conventional techniques such as artificial neural network (ANN). However, the novelty of this approach lies in that it provides explicit spline functions showing the correlation between the selected features and the TTC value. In addition, the spline expression for every single feature against TTC is obtained, revealing highly nonlinear sensitivity of the related parameters. A numerical example is presented using the modified New England 39-bus test system.
ISSN:1944-9933
DOI:10.1109/PESGM.2016.7741828