Abstract:
The increasing electric power demand invites the addition of new and renewable energy sources
in the power system. The renewable technologies consist of several power converters to achieve
the desired operation. Hence the number of power electronic converters in the power system are
also increasing day by day. The reliability of the system gets affected by the increased addition of
power electronic converters. So, the role of power converters in determining the reliability of the
system is inevitable. The conventional reliability evaluation methods consider the failures caused
by power converters as constant. The system addressed here employs a stratified procedure for
reliability analysis of a power system network. The reliability model of the system is developed
based on the power electronic devices it is composed of and the optimal value of the factors that
affect reliability is computed using genetic algorithm. Apart from finding the reliability of the
system, a reliability prediction using machine learning regression technique is done. Three test
systems including a three-bus system, Roy Billinton Test System and IEEE RTS 24 Bus system
are simulated in ETAP software and their reliability analysis is performed to verify the results
obtained
