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With the growing penetration of power electronic converters in power systems, the issue of reliability becomes more critical than ever before. This paper proposes a hierarchical reliability framework to evaluate the electric power system reliability from the power electronic converter level to the overall system level. On the converter level, the reliability model of a power electronic converter is developed based on the power electronic devices it is composed of, for which various hourly based input profiles and converter topologies are considered. On the system level, reliability metrics such as expected energy not served (EENS) and loss of load expectation (LOLE) are estimated through a non-sequential Monte Carlo simulation. Machine learning regression models, such as support vector regression (SVR), and random forests (RF) are implemented to bridge the nonlinear reliability relationship between two levels. The proposed framework is demonstrated through the modified IEEE Reliability Test System (RTS) 24-bus network. Numerical results show power converter reliability should be considered as an important factor when evaluating overall system reliability performance. [ABSTRACT FROM AUTHOR]