Fault diagnosis of modular multilevel converter based on principal component analysis and support vector machine

Abstract

Modular multilevel converter (MMC) is widely used in DC transmission, new energy grid-connected power generation, transmission, reactive power compensation, power flow control and other fields. When the sub-module fails, detecting and locating the fault quickly and accurately is the key to improving the operational reliability of the converter. Principal component analysis (PCA) obtains the feature space of reduced dimension by extracting the principal components of the fault sample set, which is conducive to the extraction of fault features. Support vector machine (SVM) has good classification performance when applied to fault diagnosis. Combining the advantages of both, this paper takes modular multi-level converter as the research object, extracts the fault characteristics of MMC, and uses PCA algorithm to reduce dimensionality. Then, the SVM classifier is constructed, the processed fault samples are used for training, and the trained SVM classifier is used to perform fault diagnosis. Finally, a three-phase eleven-level MMC simulation model is built to simulate the method used. The results show that this method can effectively improve the diagnosis speed and accuracy of MMC fault diagnosis, and provides a reference for the application of the PCA-SVM method in the actual engineering of MMC fault diagnosis.