Open-circuit fault diagnosis of NPC inverter IGBT based on independent component analysis and neural network

Hu, Hailin; Fu, Feng; Wang, Tao

doi:10.1016/j.egyr.2020.11.273

Cited by 23 publications

(8 citation statements)

References 10 publications

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“…However, the accuracy of FD for all the switch pairs is not discussed. The fault diagnostic procedures described in the literature rely on (Hu et al, 2020), (Parimalasundar and Suthanthira Vanitha, 2015) heuristic algorithms to determine the locations of faults and require a lot of training, testing data, and time to get properly trained. The architecture proposed in (Choupan et al, 2018) exploits the two bidirectional switches to tolerate the SS failure in the MLI.…”

Section: Comparative Analysismentioning

confidence: 99%

Real-time hardware-in-loop based open circuit fault diagnosis and fault tolerant control approach for cascaded multilevel inverter using artificial neural network

et al. 2023

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Multilevel inverters (MLI) are finding widespread in various engineering and commercial applications owing to their immense performance. The cascaded H-bridge (CHB) inverter is the most potential MLI topology for renewable energy applications. The successful operation of the CHB-MLI depends on the integrity of the semiconductor devices and capacitors. Irrespective of its benefits the huge number of switches decreases the reliability of the inverter. Concerning reliability, this article proposes a fault-tolerant (FT) CHB MLI for solar photovoltaic applications. The proposed CHB MLI can withstand both the single and multiple open circuit faults in all the H-bridges of the CHB topology. The diagonally opposite switch pairs of CHB topology have similar fault features which lead to difficulty in finding the fault switches using the analytical fault diagnosis methods. Hence an artificial intelligence (AI) based fault diagnosis (FD) and FT operation of CHB MLI are interpreted. The proposed model offers complete FD and FT operation within one fundamental cycle which is advantageous relative to the existing methods. Compared to the existing methods, the proposed AI-based fault diagnosis strategy achieves a shorter diagnosis time and provides 96% classification accuracy between various fault conditions. Further, the simulation and HIL results demonstrated that the voltage magnitude and THD have been maintained at 8.24% before and after the fault state. In addition, the suggested FT structure ensures the constant output power over the post-fault operation for both single and multiple switch failure instances while improving the MLI resilience. The feasibility and performance of the proposed method have been investigated through related case studies using simulation and hardware-in-the-loop (HIL) tests on a single-phase fifteen-level CHB MLI.

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Section: Comparative Analysismentioning

confidence: 99%

Real-time hardware-in-loop based open circuit fault diagnosis and fault tolerant control approach for cascaded multilevel inverter using artificial neural network

et al. 2023

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“…The results show that the recognition performance of SVM and MLP is better. For detecting faults in NPC inverter, Cheng et al [148] designs the least squares support vector machine with gradient information (G-LS-SVM) diagnostic model to classify the fault voltage signal sparsely represented by compressive sensing theory, Chen et al [149] uses multi-layer SVM to identify the upper, middle and down bridge voltages signals and differently Hu et al [150] uses a neural network as a fault classification method. It can be seen from the trend that researchers increasingly like to use the deep analysis of the neural networks to detect the open circuit fault of the inverter [151]- [154].…”

Section: Ai Approaches For Detecting Faults Of Invertermentioning

confidence: 99%

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Zhang

Deng

et al. 2022

IEEE Access

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Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

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“…IGBT is another key component in inverter, and the damage of a single IGBT may lead to the failure of the whole inverter. In view of this, Hu et al [129] proposed a novel method to detect the location of the failed IGBT.…”

Section: Fault Diagnosis Of Frequency Convertermentioning

confidence: 99%

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

Ding

Yao²,

et al. 2022

Sustainability

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Nowadays, cold ironing technology has been demonstrated to be an effective solution to deal with the environmental and social problems brought by port ship emissions and relevant effects. The working states of cold ironing equipment, especially the key components such as circuit breakers, transformers and frequency converters, have a significant effect on the safety and reliability of the whole system. However, due to the harsh working environment of cold ironing equipment, they are prone to a high risk of failure. In this respect, fault diagnosis methods can play a significant role in detecting potential failure in time and guarantee the safe and reliable operation of the cold ironing system. In recent years, research on the fault diagnosis of a cold ironing system has been rapidly growing, and this paper aims to present a comprehensive review of this literature, with an emphasis on the fault diagnosis technology applied to the key components in a cold ironing system. This review classifies the literature according to the type of key component, and, for each special type of component, the fault diagnosis methods are further categorized and analyzed. This paper provides useful references for professionals and researchers working on the fault diagnosis of a cold ironing system and points out valuable research directions in the future.

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Open-circuit fault diagnosis of NPC inverter IGBT based on independent component analysis and neural network

Cited by 23 publications

References 10 publications

Real-time hardware-in-loop based open circuit fault diagnosis and fault tolerant control approach for cascaded multilevel inverter using artificial neural network

Real-time hardware-in-loop based open circuit fault diagnosis and fault tolerant control approach for cascaded multilevel inverter using artificial neural network

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

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