Submodule Voltage Sensor Faults Diagnosis in Modular Multilevel Converters

Modular Multilevel Converters (MMCs) have emerged as a key technology for grid-integrated renewable energy systems in high-power applications due to their superior performance and scalability. The effective operation of an MMC for stable and reliable renewable energy integration is crucial and depends on its controller. The MMC controller relies on feedback from submodule (SM) capacitor voltage sensors, as well as voltage and current sensors installed in each arm and on the direct current (DC) and alternating current (AC) sides of the converter. However, the failures or divergence of these sensory devices from their normal operation can affect the MMC operation, leading to capacitor voltage imbalance, increased circulating currents, and reliability issues. Therefore, this paper proposes a model-based fault diagnosis algorithm to timely and accurately detect multiple SM sensor faults in the MMC system. Firstly, an online estimator with time-varying estimator gain matrix is developed for SM capacitor voltages estimation, considering the effects of sensor noise and uncertainty. Secondly, the online estimator is utilized to design the SM voltage sensor fault diagnosis scheme based on analytical redundancy relationships, established through pairs of residual signals and adaptive thresholds. Finally, the proposed SM sensor fault diagnosis scheme has been validated through simulation case studies, showing its effectiveness in diagnosing sensor faults and in improving the reliability and safety of MMCs.