Deep Convolution Neural Network Based Fault Detection and Identification for Modular Multilevel Converters

et al. 2022

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.

Section: Comparisons Of Resultsmentioning

confidence: 95%

Section: Comparisons Of Resultsmentioning

confidence: 95%

Section: Comparisons Of Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission

et al. 2022

“…Deep learning methods can avoid the problems of feature extraction, but the related publications are very limited in the application of MMC-HVDC systems. Convolutional Neural Networks (CNN) [ 30 , 31 ], and 1-D CNN [ 32 ] are proposed for fault classification and fault location in MMC-HVDC. Our research group proposed CNN, AutoEncoder-based deep neural network (AE-based DNN), and SoftMax classifier for MMC [ 33 ], the results showed that these deep learning methods have good potential.…”

Section: Introductionmentioning

confidence: 99%

Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method

et al. 2021

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion, but it needs more training time.

“…Kiranyaz et al [26] use 1-D CNN to detect and localize the switch open-circuit fault using four cell capacitor voltage, circulating current and load current signals. This method can achieve a detection probability of 0.989 and an average identification probability of 0.997 in less than 100 ms. Qu et al [27] propose CNN for MMC fault detection using each capacitor's voltage signal. Wang et al [28] propose CNN for DC fault detection and classification using wavelet logarithmic energy entropy of transient current signal.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods

et al. 2020

In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone SoftMax classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower bridges’ currents of the MMCs are used directly in our proposed approaches without any explicit feature extraction or feature subset selection. The two-terminal MMC-HVDC system is implemented in Power Systems Computer-Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) to verify and compare our methods. The simulation results indicate CNN, AE-based DNN, and SoftMax classifier can detect and classify faults with high detection accuracy and classification accuracy. Compared with CNN and AE-based DNN, the SoftMax classifier performed better in detection and classification accuracy as well as testing speed. The detection accuracy of AE-based DNN is a little better than CNN, while CNN needs less training time than the AE-based DNN and SoftMax classifier.