A Data-Driven Condition Monitoring Method for Capacitor in Modular Multilevel Converter (MMC)

TL;DR

This paper introduces a data-driven approach using particle swarm optimization to simultaneously monitor capacitance and ESR of capacitors in MMCs, enhancing reliability and enabling predictive maintenance.

Contribution

It proposes a novel method that estimates both capacitance and ESR simultaneously, improving over existing single-parameter monitoring techniques.

Findings

Effective estimation of capacitance and ESR demonstrated through simulations.

Accurate prediction of sub-module voltage validated experimentally.

Method enhances reliability and predictive maintenance in MMCs.

Abstract

The modular multilevel converter (MMC) is a topology that consists of a high number of capacitors, and degradation of capacitors can lead to converter malfunction, limiting the overall system lifetime. Condition monitoring methods can be applied to assess the health status of capacitors and realize predictive maintenance to improve reliability. Current research works for condition monitoring of capacitors in an MMC mainly monitor either capacitance or equivalent series resistance (ESR), while these two health indicators can shift at different speeds and lead to different end-of-life times. Hence, monitoring only one of these parameters may lead to unreliable health status evaluation. This paper proposes a data-driven method to estimate capacitance and ESR at the same time, in which particle swarm optimization (PSO) is leveraged to update the obtained estimations. Then, the results of the estimations are used to predict the sub-module voltage, which is based on a capacitor voltage equation. Furthermore, minimizing the mean square error between the predicted and actual measured voltage makes the estimations closer to the actual values. The effectiveness and feasibility of the proposed method are validated through simulations and experiments.