Machine Learning Based Operating Region Extension of Modular Multilevel Converters Under Unbalanced Grid Faults

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Abstract
The capacitor voltage ripples of the modular multilevel converter (MMC) are increased under unbalanced grid fault conditions. Since high capacitor voltage ripples deteriorate their lifetimes and may even cause tripping of the MMC system, it is important to restrict them. To this end, it is well known that injecting double fundamental frequency circulating currents can reduce the capacitor voltage ripples. However, finding a proper circulating current reference to achieve desired ripples analytically is complicated. This paper proposes an alternative method to quickly calculate the proper circulating current references without analytical computations, which is achieved by an artificial neural network (ANN) trained to approximate the relationship between circulating current references and capacitor voltage ripples. The training data is firstly extracted from a detailed simulation model of the MMC. Afterwards, the ANN is trained by the input-output data to obtain the mapping relationship, which is then used to derive the desired circulating current references. Both the simulation and the experimental results verify the practicability of the proposed method, where the operating region can be extended 30% at a minimum in all testing conditions.

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