A comparison of various open-circuit fault detection methods in the IGBT-based DC/AC inverter used in electric vehicle

机译:电动车辆中使用的基于IGBT的DC / AC逆变器中各种开路故障检测方法的比较

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Fault detection can increase the reliability and efficiency of power electronic converters employed in the power systems. Among the converters in the power system, voltage source inverters are used to drive electric motors. Due to high pressure and complex work in this environment, these inverters are prone to breakdowns and faults. That's why providing a way to recognize faults in the inverters is very important. This article studies the open-circuit fault of IGBT in an electric vehicle in dynamic conditions. The three-phase current and wavelet transform is used to identify the state of the system and extracting the current waveform. MLP Neural network algorithms, SVM, SOM, and K-means is used to detect and classify the faults. A comparison of various algorithms used in this paper was done. Electric vehicles have been studied and analyzed in 5 dynamics and 5 static modes in a total of 220 fault cases. The results show the detection and recognition of all forms of faults.
机译:故障检测可以提高电力系统中采用的电力电子转换器的可靠性和效率。 在电力系统的转换器中,电压源逆变器用于驱动电动机。 由于这种环境中的高压和复杂的工作,这些逆变器容易发生故障和故障。 这就是为什么提供一种方法来识别逆变器中的故障非常重要。 本文在动态条件下研究了电动汽车中IGBT的开路故障。 三相电流和小波变换用于识别系统的状态并提取电流波形。 MLP神经网络算法,SVM,SOM和K-inse用于检测和分类故障。 完成了本文中使用的各种算法的比较。 在5个动态和5种静态模式中研究和分析了电动车辆,总共220个故障情况。 结果显示了所有形式的故障的检测和识别。



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