New Methods for Reliability Evaluation and Enhancement of Power Systems

Modern Power systems are smart, interconnected, interdependent, load sharing and phased mission systems. Reliability of such complex power systems is very important in design, planning, installation and maintenance to provide electrical energy as economical as possible with an acceptable degree of reliability. In this thesis four new methods for reliability evaluation and enhancement of power systems are presented and further an innovative cost effect cloud service based smart early warning system using machine to machine (M2M) technology to improve the reliability of power systems is presented. Many fielded power systems use cold standby redundancy as an effective design strategy for improving system reliability. However, methods for analysing the reliability of k-out-of-n cold standby systems, particularly with components having age-dependent hazard (failure) rates, are limited. In this thesis the first method is proposed using the concepts of counting processes, an efficient approximate method to evaluate the reliability of ‘k-out-of-n’ cold standby systems is proposed. This proposed new method considers Rayleigh distributions for component life times and the effects of switch failures on system reliability. The main advantage of this counting process-based method is that it reduces a complex problem involving multiple integrals into an equivalent simple problem involving one-dimensional convolution integrals. Further eliminates the