Fault diagnosis in a five-level multilevel inverter using an artificial neural network approach

E. Parimalasundar, R. Senthil Kumar, V. S. Chandrika, K. Suresh
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引用次数: 18

Abstract

Introduction. Cascaded H-bridge multilevel inverters (CHB-MLI) are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of CHB-MLI. This paper investigates the effects and performance of open and short switching faults of multilevel inverters. Output voltage characteristics of 5 level MLI are frequently determined from distinctive switch faults with modulation index value of 0.85 is used during simulation analysis. In the simulation experiment for the modulation index value of 0.85, one second open and short circuit faults are created for the place of faulty switch. Fault is identified automatically by means of artificial neural network (ANN) technique using sinusoidal pulse width modulation based on distorted total harmonic distortion (THD) and managed by its own. The novelty of the proposed work consists of a fast Fourier transform (FFT) and ANN to identify faulty switch. Purpose. The proposed architecture is to identify faulty switch during open and short failures, which has to be reduced THD and make the system in reliable operation. Methods. The proposed topology is to be design and evaluate using MATLAB/Simulink platform. Results. Using the FFT and ANN approaches, the normal and faulty conditions of the MLI are explored, and the faulty switch is detected based on voltage changing patterns in the output. Practical value. The proposed topology has been very supportive for implementing non-conventional energy sources based multilevel inverter, which is connected to large demand in grid.
基于人工神经网络的五电平多电平逆变器故障诊断
介绍。级联h桥多电平逆变器(CHB-MLI)越来越多地应用于配电系统、电力牵引系统、高压直接转换系统等领域。尽管事实上,多电平逆变器包含大量的控制开关,检测故障需要大量的时间。在故障开关配置中,包括用于在开路故障状态下自由操作的二极管。短路时的故障情况是由熔断器进行的,它可以显示电流的自由流向。CHB-MLI的功能和可靠性影响了电源开关的故障,可独立识别故障类别和故障。研究了多电平逆变器开路和短开关故障对系统性能的影响。5级MLI的输出电压特性往往是由特殊的开关故障确定的,仿真分析时采用调制指数值为0.85。在调制指标值为0.85的仿真实验中,在开关故障处产生1秒开路和短路故障。采用基于畸变总谐波失真(THD)的正弦脉宽调制技术,采用人工神经网络技术实现故障的自动识别和自动管理。该方法的新颖之处在于采用快速傅里叶变换(FFT)和人工神经网络来识别故障开关。目的。所提出的体系结构是在开路故障和短路故障时识别故障开关,从而降低系统的THD,使系统可靠运行。方法。该拓扑将在MATLAB/Simulink平台上进行设计和评估。结果。利用FFT和ANN方法,探讨了MLI的正常和故障情况,并根据输出电压变化模式检测故障开关。实用价值。所提出的拓扑结构为实现基于非常规能源的多电平逆变器提供了有力的支持。
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