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Due to the possibility of putting a large number of modules consisting of switches and capacitors connected in series, the modular multilevel converter (MMC) can easily be scaled to high power and high voltage power conversion, which is an attractive feature for filter-less and transformer-less design and helpful to achieve high efficiency. However, a significantly increased amount of sub-modules in a MMC may increase the requirements for sensors and also increase the risk of failures. As a result, fault detection and diagnosis of MMC sub-modules are of great importance for continuous operation and post-fault maintenance. Therefore, in this paper, an effective fault diagnosis technique for real-time diagnosis of the switching device faults covering both the open-circuit faults and the short-circuit faults in MMC sub-modules is proposed, in which the faulty phase and the fault type is detected by analyzing the difference among the three output load currents, while the localization of the faulty switches is achieved by comparing the estimation results by the adaptive observer. In contrast to other methods that use additional sensors or devices, the presented technique uses the measured phase currents only, which are already available for MMC control. In additional, its operation, effectiveness and robustness are confirmed by simulation results under different operating conditions and load conditions.

Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems