Fault Diagnosis for Multilevel Converters Based on an Affine-Invariant Riemannian Metric Autoencoder

Zhang, Feng; Gao, Fei; Zhou, Zhongzheng; Yang, Yayu

doi:10.1109/tii.2022.3186992

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…The classification accuracy achieved using CMLI is 95.56%, and that of Packed Ucell (PUC) inverters is 94.28% [20]. Affine-Invariant Riemannian Metric Autoencoder Random Forest (AIRMAR) is proposed to recognize OCF in MLI [21].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Asymmetric Cascaded Multilevel Inverter using Ensemble Machine Learning

2024

Informacije MIDEM

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Cascaded Multi-Level Inverters (CMLI) are used in a wide range of high-power industrial drives and for integrating solar PV system. Asymmetric Cascaded Multilevel Inverter (ACMLI) produces an output voltage with reduced Total Harmonic Distortion (THD) when compared to Symmetric Cascaded Multilevel Inverter (SCMLI). ACMLI comprises of more semiconductor devices and thus reliability is a major concern. Efficient, high speed and precise fault detection is required for ACMLI to reduce failure rates and avoid unplanned shutdown. RMS voltage, mean voltage and THD under various single and double switch fault conditions are used as features for fault diagnosis. Fault diagnosis method for ACMLI based on probabilistic principal component analysis (PPCA) and Ensemble Machine Learning (EML) is presented. PPCA is used to optimize data and reduce the size of fault features. Finally, an EML classifier combining Support Vector Machine (SVM), K-Nearest Neighborhood (KNN) and Decision Tree (DT) is employed to diagnose the various open circuit faults. The proposed fault diagnosis method is validated using an experimental setup. The simulation and experimental result shows that EML technique diagnosis the fault with 99.32% accuracy.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Asymmetric Cascaded Multilevel Inverter using Ensemble Machine Learning

2024

Informacije MIDEM

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“…In [28] a data-driven fault diagnosis method of a five-level nested neutral point piloted (NNPP) converter is proposed. The authors of this paper have used, an affine-invariant Riemannian metric autoencoder (AIRMAE) for fault diagnosis of multilevel converters.…”

Section: Introductionmentioning

confidence: 99%

Switch Fault Detection and Localization for T-Type Converter

Becker

Jamshidpour

Poure

et al. 2023

IEEE Trans. on Ind. Applicat.

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Over the past decade, multilevel converters have received considerable interest in medium voltage applications. A large number of active switches in a multilevel converter increases the probability of switch failure and decreases the reliability of the system. An open switch fault can lead to a malfunction of the whole system and should be diagnosed as soon as possible. In this paper, an open-switch fault diagnosis method for a five-level H-Bridge T-type converter is presented. The proposed fault detection method and the fault localization algorithm are based on the switching patterns and the converter output voltage level observation. This approach is capable to detect and localize an open circuit fault for all switches. First, it is validated by simulations in Matlab Simulink and Simscape toolbox environments. Experimental results are also presented and discussed to validate the simulation results.

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Open-Circuit Fault Diagnosis for Grid-tied T-Type Inverters Using an LSTM Autoencoder

Wu,

Zhao

2023

2023 China Automation Congress (CAC)

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Fault Diagnosis for Multilevel Converters Based on an Affine-Invariant Riemannian Metric Autoencoder

Cited by 8 publications

References 23 publications

Fault Diagnosis of Asymmetric Cascaded Multilevel Inverter using Ensemble Machine Learning

Fault Diagnosis of Asymmetric Cascaded Multilevel Inverter using Ensemble Machine Learning

Switch Fault Detection and Localization for T-Type Converter

Open-Circuit Fault Diagnosis for Grid-tied T-Type Inverters Using an LSTM Autoencoder

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