Paper Details

The most important requirement and need for proper operation of power system is maintenance of the systemsecurity. The security assessment analysis is done to determine until what period the power system remains in the safeoperable mode. Contingency screening is done to identify critical contingencies in order to take preventive actions at theright time. The severity of a contingency is determined by two scalar performance indices: Voltage -reactive powerperformance index(????????????????) and line MVA performance index(????????????????????). Performance indices are calculated based on theconventional method known as Newton Raphson load flow program. Contingency ranking is done based on the severityof the contingencies. In this proposed work, contingency analysis is done with IEEE 14 bus. Since the system parametersare dynamic in nature and keeps on changing, there is need of soft computing technologies. Supervised learningapproach that uses Feed-Forward Artificial Neural Network(FFNN) is employed using pattern recognition methodologyfor security assessment and contingency analysis. A feature selection technique based on the correlation coefficient hasbeen employed to identify the inputs for thee FFNN. With these soft computing techniques, greater accuracy is achieved.

SreedeviSreekumaran, Lekshmi Nair, H.nagesh, “POWER SYSTEM SECURITY ASSESSMENT AND CONTINGENCY ANALYSIS USING SUPERVISED LEARNING APPROACH”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 4, pp. 637-646, April 2015.