Monolithic Data-Driven Condition Monitoring Strategy for MMC Considering $C$ and ESR

Reliability of capacitors is a critical factor in ensuring the optimal performance of modular multilevel converters (MMC), which can be enhanced through health status monitoring and predictive maintenance. However, existing literature on MMC submodule capacitor condition monitoring primarily focuses on capacitance while overlooking the influences of the equivalent series resistance (ESR). Furthermore, the second problem is that the ESR is assumed to be negligible in the capacitance estimation. The assumption is not always valid, and may introduce significant errors in capacitance estimation. To improve existing condition monitoring methods, this article uses a capacitor–voltage equation to model the coupling effect of capacitance and ESR, based on which, particle swarm optimization is used to update the estimations of capacitance and ESR together. The proposed method offers more reliable health monitoring with two health indicators and derives a better estimation accuracy when the ESR is not negligible. Furthermore, to ensure the existence of the global optimal solution, the convexity of the data-driven problem is studied. The effectiveness and feasibility of the proposed method are validated with simulations, experiments, and an open-source dataset.