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As one important means of ensuring secure operation in a power system, thecontingency selection and ranking methods need to be more rapid and accurate. Anovel method-based least absolute shrinkage and selection operator (Lasso)algorithm is proposed in this paper to apply to online static securityassessment (OSSA). The assessment is based on a security index, which isapplied to select and screen contingencies. Firstly, the multi-step adaptiveLasso (MSA-Lasso) regression algorithm is introduced based on the regressionalgorithm, whose predictive performance has an advantage. Then, an OSSA moduleis proposed to evaluate and select contingencies in different load conditions.In addition, the Lasso algorithm is employed to predict the security index ofeach power system operation state with the consideration of bus voltages andpower flows, according to Newton-Raphson load flow (NRLF) analysis inpost-contingency states. Finally, the numerical results of applying theproposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systemsdemonstrate the accuracy and rapidity of OSSA.

Online Static Security Assessment of Power Systems Based on Lasso Algorithm