Fault Detection Method Using a Convolution Neural Network for Hybrid Active Neutral-Point Clamped Inverters

Abstract

This article presents an open-switch fault detection method for a hybrid active neutral-point clamped (HANPC) inverter based on deep learning technology. The HANPC inverter generates a three-level output voltage with four silicon switches and two silicon carbide switches per phase. The probability of open fault in switching devices increases because of the large number of switches of the entire power converter. The open-switch fault causes distortion of output currents. A convolution neural network (CNN) comprising several convolution layers and fully connected layers is used to extract features of distorted currents. A CNN network was trained using three-phase current information to determine the location of the open-switch fault. Our proposed CNN model can accurately detect approximately 99.6% of open-switch faults without requiring additional circuitry and regardless of the current level within an average time of 1.027ms. The feasibility and effectiveness of the proposed method are verified by experimental results. INDEX TERMS Open-switch fault detection, hybrid active neutral-point inverter, silicon carbide, deep learning, convolution neural network.

Failure Prevention in DC–DC Converters: Theoretical Approach and Experimental Application on a Zeta Converter

“…In the literature, there are some works related to the monitoring of power converters using machine learning algorithms [17], [18]. One of the most critical aspects is the number of measurements required to identify different failure scenarios and the absence of the prevention aspect.…”

Hybrid Condition Monitoring for Power Converters: Learning-Based Methods With Statistical Guarantees

“…Recently, convolutional neural networks (CNNs) have also shown good results in isolating OC faults by using the wavelet transform to extract features from the measured arm and submodule signals [33]. [34] showed how phase-current signals can be used with CNNs for FDI of single OC faults in a three-phase inverter. A very robust classier based on a 1-dimensional CNN is designed in [35], using sub-module output voltages and circulating current as the inputs to the network.…”

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

“…In another valuable study [69], the CNN method has been selected as a diagnostic tool to detect inverter faults in the PV system and symmetrical/unsymmetrical faults in the distribution line. In [70], the CNN procedure has been represented as a powerful tool to diagnosis OC switch fault in a hybrid active NPC inverter. The ELM and Random Vector Functional Link network techniques, as machine learning applications, identify and classify OC fault in an IGBT utilized in a 3-phase PWM converter [71].…”

“…Time-series mode modeling of input data is also not possible using this method. [59] Switch OC and SC [68] Switch OC Back-to-back converter in permanent magnet synchronous generator-based wind generation system [69] Switch SC 5-level neutral-point-clamped voltage source inverter connected to PV integrated microgrid system [70] Switch OC Hybrid active NPC inverter [74] Power There are different types of sensors related to measuring voltage and current, which are classified based on their performance and method of measurement, which the continuation of this section introduces each of them.…”