Modular Multilevel Cascade Converters (MMCCs) are considered a promising power electronics topology in industry. Their scalability allows to reach (ultra/very) high voltage levels with low harmonic content and high efficiency and makes MMCCs an ideal solution for high-power applications; such as electrical drives, solid-state transformers and high-voltage direct-current (HVDC) transmission systems. However, the high levels of thermal, electrical and mechanical stress on the power electronics devices and the large number of components (e.g. capacitors or semiconductors) make MMCCs prone to faults. Fault detection and diagnosis (FDD) in combination with fault isolation and system reconfiguration techniques, based on cell redundancy, can increase the reliability, availability and safety of MMCCs, which is crucial for their utilization in critical energy applications. This second part of the paper comprehensively surveys: (i) fault tolerance and fault detection & diagnosis (FDD; e.g. expert system, model-or hardware and data-based FDD methods) and (ii) system reconfiguration strategies (e.g. cold-or hot-redundant) for MMCCs. Finally, the state-of-the-art, challenges and future research trends and opportunities towards reliable MMCC-based systems are revealed.

Modular Multilevel Cascade Converters (MMCCs) are considered a promising power electronics topology in industry. Their scalability allows to reach (ultra/very) high voltage levels with low harmonic content and high efficiency and makes MMCCs an ideal solution for high-power applications; such as electrical drives, solid-state transformers and high-voltage direct-current (HVDC) transmission systems. However, the high levels of thermal, electrical and mechanical stress on the power electronics devices and the large number of components (e.g. capacitors or semiconductors) make MMCCs prone to faults reducing its reliability. In this first part of the paper, a comprehensive overview of reliability on MMCCs, failure mechanisms and fault impact analysis in MMCCs, including failure rates and fault modes is presented. Also a set of tables which collect all information to easily detect and identify faults in MMCCs is presented.