A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM) to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs): line MVA performance index (PI MVA ) and Voltage-reactive power performance index (PI VQ ). SVM works in two steps. Step I is the estimation of bothdoi:10.1080/23311916.2015.1137201 fatcat:vwzehljqtjhmbhbkola2wn4cma