The Fault Detection, Localization, and Tolerant Operation of Modular Multilevel Converters with an Insulated Gate Bipolar Transistor (IGBT) Open Circuit Fault

Li, Wei; Li, Gengyin; Zeng, Rong; Ni, Kai; Hu, Yihua; Wen, Huiqing

doi:10.3390/en11040837

Cited by 12 publications

(9 citation statements)

References 31 publications

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“…A Tektronix 5 series mixed-signal oscilloscope, which uses FlexChannel-based input and contains 4, 6 and 8 channel modes is used to collect the voltage and current data, as shown in Figure 9. The features of electronic equipment in recent research were mainly extracted from the signals of voltage and current [43], [48]- [52], and the voltage signal was frequently used [50]. The output voltage of ATRUs was chosen as the basic input of the SDCNN for the fault diagnosis of ATRUs.…”

Section: A Description Of Airborne Atrumentioning

confidence: 99%

Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis

Chen

Jing

et al. 2019

IEEE Access

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Among deep learning methods, convolutional neural networks (CNNs) are able to extract features automatically and have increasingly been used in intelligent fault diagnosis studies. However, studies seldomly concentrate on the weakness associated with a highly imbalanced distribution of fault types due to different failure rates and when multiple faults are easily confused with single faults. To solve these problems, this paper developed a stochastic discrete-time series deep convolutional neural network (SDCNN) method based on random oversampling along with a progressive method with multiple SDCNNs to improve the diagnosis performance. To assess the developed method, datasets from three avionics 24-pulse autotransformer rectifier units (ATRUs), which are secondary electric power supplies in aircraft, were analyzed and compared with other CNN methods. INDEX TERMS Fault diagnosis, convolutional neural network, imbalanced classification, confusable fault types.

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Section: A Description Of Airborne Atrumentioning

confidence: 99%

Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis

Chen

Jing

et al. 2019

IEEE Access

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“…This type of failure induces constraints in the operation of the EV if the driver is not notified and can eventually result in a stop that can be even dangerous, leading to accidents. Such a defect can cause secondary failures in other vehicle components that can lead to high repair costs [27].…”

Section: Introductionmentioning

confidence: 99%

A Sensor-Based System for Fault Detection and Prediction for EV Multi-Level Converters

Prejbeanu

2023

Sensors

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Power electronic converters and alternating current motors are the actual driving solution applied to electric vehicles (EVs). Multilevel inverters with high performance are modern and the basis for powering and driving EVs. Fault component detection in multilevel power converters requires the use of a smart sensor-based strategy and an optimal fault analysis and prediction method. An innovative method for the detection and prediction of defects in multilevel inverters for EVs is proposed in this article. This method is based on an algorithm able to determine in a fast and efficient way the faults in a multilevel inverter in different possible topologies. Moreover, the fault detection is achieved not only for a single component, but even for several components, if these faults occur simultaneously. The detection mechanism is based on the analysis of the output current and voltage from the inverter, with the possibility of distinguishing between single and multiple faults of the power electronic components. High-performance simulation programs are used to define and verify the method model. Additionally, with this model, harmonic analysis can be performed to check the correctness of the system’s operation, and different fault scenarios can be simulated. Thus, significant results were obtained by simulation on various topologies of multilevel converters. Further, a test bench was developed in order to verify some failure situations on a three-level inverter.

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“…Therefore, the accuracy of the fault diagnosis is based on the amount of learning data and the correctness of the learning process. In [20], an effective control strategy including fault detection, localization, and tolerant operation was proposed for a modular multi-level converter with a transistor open-circuit fault; however, this method is only applicable to a large number of series-connected two-level converter modules, and only for AC-to-DC converters. A novel fault-tolerant control strategy was proposed in [21] for bypassing converter submodules (SM) with faults and re-regulating the SM capacitor voltage and carrier phase-shift angle to maintain the main components of circulating current, additionally reducing the total harmonic distortion (THD) of the grid-connected current to enable the stable operation of the photovoltaic inverter; however, this method is also only suitable for a large number of series-connected two-level inverters.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives

Chao

2020

Energies

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This paper presents an extension theory-based assessment method to perform fault diagnosis for inverters in motor driving systems. First, a three-level neutral-point clamped (NPC) inverter is created using the PSIM software package to simulate faults for any power transistor in the NPC-type inverter. Fast Fourier transformation is used to transform the line current signals in the time domain into a spectrum in the frequency domain for analysis of the corresponding spectrum of features of the inverter for faults with different power transistors. Then, the relationships between fault types and specific spectra are established as characteristics for the extension assessment method, which is then used to create a smart fault diagnosis system for inverters. Fault-tolerant control (FTC) is used here when the rated output of a faulty inverter is decreased in order to maintain balanced output in three phases by changing the framework of the transistor connection. This is performed to reinforce the reliability of the inverter. Finally, by the simulation and experimental results, the feasibility of the proposed smart fault diagnosis system is confirmed. The proposed fault diagnosis method is advantageous due to its minimal use of data and lack of a learning process, which thereby reduces the fault diagnosis time and makes the method easily used in practice. The proposed fault-tolerant control strategy allows both online and smooth switching in the wiring structure of the inverter.

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The Fault Detection, Localization, and Tolerant Operation of Modular Multilevel Converters with an Insulated Gate Bipolar Transistor (IGBT) Open Circuit Fault

Cited by 12 publications

References 31 publications

Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis

Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis

A Sensor-Based System for Fault Detection and Prediction for EV Multi-Level Converters

Fault Diagnosis and Tolerant Control of Three-Level Neutral-Point Clamped Inverters in Motor Drives

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