Details

Autor(en) / Beteiligte

• Lv, Jiaqing

Titel

Transient stability assessment in large-scale power systems based on the sparse single index model

Ist Teil von

• Electric power systems research, 2020-07, Vol.184, p.106291, Article 106291

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2020

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •This paper applies a novel artificial intelligence approach to the transient stability prediction problem.•In our modeling approach, the mapping between bus voltage features and the CCT is modeled as a single index model (SIM).•Iterative methods combing Lasso and nonparametric regression are applied to solve the SIM model.•Automatic feature selection is achieved in the process of the regression analysis. The resultant model processes a parsimonious structure.•For two fault cases in a 470-bus network, our modeling approach is compared with 1) the kernel ridge regression and 2) the Lasso linear method. We apply a class of nonlinear semiparametric models to the problem of transient stability analysis of a large-scale power system. The mapping between the pre-contingency state and the transient stability boundary (TSB) is modelled by a block-oriented structure known as the single-index model (SIM). In this model one has a single dimensional projection that enters the unknown nonlinearity nonparametrically. Such models form a rich class of nonlinear mappings that includes classical models, e.g., linear and logistic regression, as special cases. The generalized case of the SIM is utilized that is taking into account the sparsity of the projection vector. This yields a low-dimensional nonlinear model suitable for high-dimensional data emerging in modern power systems. The parametric part of the model is obtained by the properly modified sparsity sensitive LASSO algorithm. The nonlinear nonparametric part of the model is estimated by the monotonically corrected kernel regression estimate. The precision of our modeling is verified for two fault cases of the 470-bus power system. It is shown that for the examined faults, the proposed modeling methodology exhibits a stronger prediction accuracy compared to the existing competing methods recently applied in the field, namely, kernel ridge regression, as well as LASSO linear modeling.