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

A Diagnosis Strategy for Multiple IGBT Open-Circuit Faults of Modular Multilevel Converters

2021 ◽  
Vol 36 (1) ◽  
pp. 191-203
Author(s):  
Xingxing Chen ◽  
Jinjun Liu ◽  
Zhifeng Deng ◽  
Shuguang Song ◽  
Sixing Du ◽  
...  
Keyword(s):  
Open Circuit ◽  
Multilevel Converters ◽  
Modular Multilevel Converters

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

scholarly journals A Complex Fault Diagnostic Approach of Active Distribution Network Based on SBS-SFS Optimized Multi-SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Keyan Liu ◽  
Weijie Dong ◽  
Huanyu Dong ◽  
Jia Wei ◽  
Shiwu Xiao
Keyword(s):  
Fault Diagnosis ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Superior Performance ◽  
Diagnostic Approach ◽  
Fault Identification ◽  
Support Vector ◽  
Distributed Power ◽  
Fault Type ◽  
Fault Diagnostic

After renewable energy distributed generator (DG) is connected to the power grid, traditional diverse-electric-information-based fault diagnosis approaches are not suitable for an active distributed network (ADN) due to the weak characteristics of fault current. Thus, this paper proposes a comprehensive nonformula fault diagnostic approach of ADN using only voltage as input. In the preprocess, sequential forward selection (SFS) and sequential backward selection (SBS) are utilized to optimize the input feature matrix of the sample in order to reduce the information redundancy of multiple measuring points in ADN. Then, a single “1-a-1” support vector machine (SVM) classifier is used for fault identification, and a multi-SVM, with radial basis function (RBF) as the kernel function, is applied to identify the location and fault type. To prove the proposed method is adaptable for ADN, two direct drive fans are used as a DG to test the IEEE 33 node model at every 10% of the line under three operating conditions that include all cases of distributed power generation in ADN. Results comparing real-time and historical data show that the proposed multi-SVM model reaches an average fault type diagnosis accuracy of 97.27%, with a fault identification accuracy of 96%. A backpropagation neural network is then compared to the proposed model. The results show the superior performance of the SBS-SFS optimized multi-SVM. This model can be usefully applied to the fault diagnosis of new energy sources with distributed power access to distribution networks.

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