Fault Diagnosis for Multilevel Converters Based on an Affine-Invariant Riemannian Metric Autoencoder

Multilevel converters play an important role in power electronic systems. In recent years, data-driven fault diagnosis technologies for multilevel converters have rapidly developed and are generally based on feature engineering and intelligent classification algorithms. However, the fault detection rate, speed, and stability still need to be improved, especially for complex converter systems. In this article, based on a probabilistic autoencoder architecture, an affine-invariant Riemannian metric autoencoder (AIRMAE) is proposed for feature extraction, and combined with classifiers, a supervised learning fault diagnosis method for multilevel converters is constructed. The proposed AIRMAE is able to investigate low-dimensional representations from high-dimensional data manifolds while preserving sufficient valid information. Based on a five-level nested neutral-point piloted converter, the effectiveness of the proposed method is verified by experiments. The performance is superior to that of existing state-of-the-art fault diagnosis methods, with high detection accuracy, good stability, and strong waveform reconstruction ability.