Reliability Assessment of Electric Power Systems with Smart Monitoring Using Monte Carlo Simulation

Abstract

This thesis proposes a Markov model that combines smart monitoring with electrical components to analyze overall power system reliability. The Markov model accounts for the reliability factors of smart monitoring such as failure rates, fault detection rates, and repair rates to demonstrate the effectiveness of smart monitoring in reducing component failures. The Markov absorbing probability values are derived and used in IEEE RTS 79 to evaluate the impact of integrating smart monitoring on power system reliability. Through Sequential Monte Carlo Simulation, power system reliability indices such as LOLP, EDNS and EENS are calculated. The test cases consist of three scenarios based on the IEEE RTS 79: the original test system, the test system considering substation failures, and the test system integrating smart monitoring. The simulation results verify the significant influence of substation failures and the benefits of smart monitoring on the power system reliability.