Diagnosis and location of the open-circuit fault in modular multilevel converters: An improved machine learning method

Abstract

In this paper, the fault diagnosis and location (FDL) problem of the open circuit fault for modular multilevel converter (MMC) is investigated. A mixed kernel support tensor machine (MKSTM) is provided, and it’s employed to improve the support tensor machine which is an important algorithm of machine learning. By extracting the characteristic data of ac current and internal circulation current in either normal operation or open-circuit fault, then training and classifying the obtained samples with MKSTM, FDL of MMC can be realized with the supplied algorithm. Finally, experimental results show that the classification accuracy of MKSTM algorithm is improved observably than single kernel function STM such as linear, Radial Basis function (RBF), sigmoid and polynomial types. Synchronously, the open-circuit fault can be effectively diagnosed and located with the proposed method.