A Data-Driven Condition Monitoring Method for Capacitor in Modular Multilevel Converter (MMC)
arxiv(2024)
摘要
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.
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