  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.
