POWER SYSTEM SECURITY ASSESSMENT AND CONTINGENCY ANALYSIS USING SUPERVISED LEARNING APPROACH
Keywords:
Contingency analysis, Static security assessment, Neural network, Feature selection, Performance indices, Pattern recognitionAbstract
The most important requirement and need for proper operation of power system is maintenance of the system
security. The security assessment analysis is done to determine until what period the power system remains in the safe
operable mode. Contingency screening is done to identify critical contingencies in order to take preventive actions at the
right time. The severity of a contingency is determined by two scalar performance indices: Voltage -reactive power
performance index(????????????????) and line MVA performance index(????????????????????). Performance indices are calculated based on the
conventional method known as Newton Raphson load flow program. Contingency ranking is done based on the severity
of the contingencies. In this proposed work, contingency analysis is done with IEEE 14 bus. Since the system parameters
are dynamic in nature and keeps on changing, there is need of soft computing technologies. Supervised learning
approach that uses Feed-Forward Artificial Neural Network(FFNN) is employed using pattern recognition methodology
for security assessment and contingency analysis. A feature selection technique based on the correlation coefficient has
been employed to identify the inputs for thee FFNN. With these soft computing techniques, greater accuracy is achieved.
