Abstract
Modular multilevel converters (MMCs) have a complex structure and a large number of submodules (SMs). If there is a fault in one of the SMs, it will affect the reliable operation of the system. Therefore, rapid fault diagnosis and accurate fault positioning are crucial to ensuring the continuous operation of the system. However, the IGBT open-circuit faults in different submodules of MMCs have similar fault features, and the traditional fault feature extraction method cannot effectively extract the key features of the fault so as to accurately locate the faulty submodules. In response to this problem, this paper proposes a fault diagnosis method based on weighted-amplitude permutation entropy (WAPE) and DS evidence fusion theory. The simulation results show that WAPE has better feature extraction ability than basic permutation entropy, and the fused multiscale feature decision output has better diagnostic accuracy than the single-scale feature. Compared with traditional fault diagnosis methods, this method achieves the diagnosis of multiple fault types by collecting a single signal, which greatly reduces the number of samples and leads to higher diagnostic accuracy and faster diagnostic speed.
Highlights
In recent years, with the rapid development of power electronics technology, as a new type of voltage source converter topology, Modular multilevel converters (MMCs) have been widely used in various engineering fields [1,2,3], such as high-voltage direct current (HVDC) transmission [4], high-voltage power drive systems [5], renewable energy [6], etc
To solve the problem of the similarity of faults in MMCs, this paper proposes a fault diagnosis method based on weighted-amplitude permutation entropy and DS evidence fusion theory
Faults, a fault diagnosis method based on weighted-amplitude permutation entropy and DS evidence fusion theory is proposed
Summary
With the rapid development of power electronics technology, as a new type of voltage source converter topology, MMCs have been widely used in various engineering fields [1,2,3], such as high-voltage direct current (HVDC) transmission [4], high-voltage power drive systems [5], renewable energy [6], etc. In [22], the authors used a stacked sparse autoencoder to extract fault features from an MMC, and used classifiers based on deep neural networks to detect faults more accurately These methods use artificial intelligence algorithms and even deep learning algorithms to classify faults, they can only locate one arm of the bridge, rather than a specific SM. The third part uses the advantages of incomplete and uncertain information from DS evidence fusion theory [23,24]—the basic probability assignment (BPA) matrix obtained by selecting permutation entropy at different scales as inputs is fused by the DS evidence fusion algorithm to obtain the final classification results This method is applied to IGBT open-circuit fault diagnosis in a specific SM. The simulation results show that, compared with other algorithms, this method has advantages in both accuracy and diagnosis time
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