Survey of switch fault diagnosis for modular multilevel converter

Abstract

This study presents a survey on the existing fault diagnosis methods (FDMs) of the switch devices for the rapidly developing modular multilevel converters (MMCs). Three categories, namely mechanism-based, signal processing-based and artificial intelligence-based FDMs, are evaluated and summarised depending on the operating principles. Mechanism-based FDMs detect the faults by comparing the inner characteristics of MMC or their derived parameters with the expected values. Signal processing-based FDMs detect the faults via comparing the processed output voltage or current with their expected values. Artificial intelligence-based FDMs detect the faults in the way of employing a trained intelligent classifier. Methods belonging to each category are introduced in detail via comparing a lot of criteria of the FDMs. Then, a figure-of-merit is defined to evaluate various FDMs. Finally, the summary is given and the developing tendency is recommended for future work.

Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method

“…Fourier decomposition method (FDM) is an adaptive decomposition method based on the theory of the Fourier transform, in which the mode mixing will not appear in that extreme value points are not required. Moreover, due to that white noises are not added in signals, there is no residual of white noises [18][19][20]. In order to solve the difficulty in extracting fault characteristics of rotating machinery under the environment with a lot of noise, a method combining FDM, robust independent component analysis (RICA), and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) is proposed and applied to the analysis of actual bearing fault signal, and the effectiveness of suppressing noise and accurately extracting fault characteristic information is obtained, which shows its practicability in the fault diagnosis of rotating machinery [21].…”

Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods

“…The research of fault detection and classification in MMC-HVDC systems applications can be broadly categorized into three basic approaches that are mechanism-based, signal processing-based, and artificial intelligence-based [5]. All the mechanism-based methods need many sensors monitoring the inner characteristics (circulating current, arm currents, capacitor voltages, etc.).…”

“…Although the NN based methods have achieved some improvements in the diagnosis of failed converters and identification of defective switches [14,15], the prerequisite for the successful application of NNs is to have enough training data and long training time. Multi-class relevance vector machines (RVM) and support vector machine (SVM) replace a neural network to classify and locate the faults, because of their rapid training speed and strongly regularized characteristic [5]. Wang et al [16] use a PCA and multiclass RVM approach for fault diagnosis of cascaded H-bridge multilevel inverter system.…”

Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

“…Power converter is a crucial device of wind energy conversion system (WECS) in a wind turbine [1]- [3].It is utilized to control the speed and torque of the generator, simultaneously, apply the active and reactive power to the grid in the modern The associate editor coordinating the review of this manuscript and approving it for publication was Ton Do .…”