Open-Circuit Fault Diagnosis and Tolerant Control for Modular Multilevel Converters

2020 ◽  
Author(s):  
Han Wang ◽  
Huiqing Wen ◽  
Rui Yang
Keyword(s):  
Fault Diagnosis ◽  
Open Circuit ◽  
Multilevel Converters ◽  
Modular Multilevel Converters ◽  
Circuit Fault Diagnosis

Submodule Voltage Similarity-Based Open-Circuit Fault Diagnosis for Modular Multilevel Converters

2019 ◽  
Vol 34 (8) ◽  
pp. 8008-8016 ◽  
Author(s):  
Dehong Zhou ◽  
Huan Qiu ◽  
Shunfeng Yang ◽  
Yi Tang
Keyword(s):  
Fault Diagnosis ◽  
Open Circuit ◽  
Multilevel Converters ◽  
Modular Multilevel Converters ◽  
Circuit Fault Diagnosis

scholarly journals Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 362
Author(s):  
Hosameldin O. A. Ahmed ◽  
Yuexiao Yu ◽  
Qinghua Wang ◽  
Mohamed Darwish ◽  
Asoke K. Nandi
Keyword(s):  
Fault Diagnosis ◽  
Multinomial Logistic Regression ◽  
Real Life ◽  
Open Circuit ◽  
Operating Conditions ◽  
Health Conditions ◽  
Support Vector ◽  
Multilevel Converters ◽  
Hvdc Transmission ◽  
Modular Multilevel Converters

Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real-life application of open-circuit failures of IGBT fault diagnosis. To achieve high classification accuracy and reduced computation time, a fault diagnosis framework with a combination of the AC-side three-phase current, and the upper and lower bridges’ currents of the MMCs to automatically classify health conditions of MMCs is proposed. In this framework, the principal component analysis (PCA) is used for feature extraction. Then, two classification algorithms—multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)—are used for classification. The effectiveness of the proposed framework is validated by a two-terminal simulation model of the MMC-high-voltage direct current (HVDC) transmission power system using PSCAD/EMTDC software. The simulation results demonstrate that the proposed framework is highly effective in diagnosing the health conditions of MMCs compared to recently published results.

Fault Diagnosis and Tolerant Control of Single IGBT Open-Circuit Failure in Modular Multilevel Converters

2016 ◽  
Vol 31 (4) ◽  
pp. 3165-3176 ◽  
Author(s):  
Binbin Li ◽  
Shaolei Shi ◽  
Bo Wang ◽  
Gaolin Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Open Circuit ◽  
Multilevel Converters ◽  
Modular Multilevel Converters

Open-Circuit Fault Diagnosis of Z-Source Inverter via Deep Neural Network

2020 ◽  
Author(s):  
Baoye Song ◽  
Shiju Nie ◽  
Xuewen Li ◽  
Lin Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Open Circuit ◽  
Circuit Fault Diagnosis
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