Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission.

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.

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.

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.

Influence of aspirin use on clinical outcomes of patients with hepatocellular carcinoma: a meta-analysis.

BACKGROUND: Aspirin use has been suggested to reduce cancer risk. However, previous studies showed inconsistent results as for the association between aspirin use and mortality in patients with hepatocellular carcinoma (HCC). The aim of the study was to evaluate the influence of aspirin use on clinical outcomes of patients with HCC in a meta-analysis. MATERIALS: Studies were obtained via systematic search of PubMed, Cochrane's Library, and Embase databases. A random-effect model, which incorporated the potential heterogeneity, was used to pool the results. RESULTS: Six retrospective cohort studies including 18,855 HCC patients that underwent liver resection or transarterial chemoembolization were included. Pooled results showed that compared to the non-users, aspirin users of HCC had significantly reduced risk of HCC recurrence (risk ratio [RR]: 0.74, 95% confidence interval [CI]: 0.59-0.93, p = 0.01; I2 = 34%) and all-cause mortality (RR: 0.59, 95% CI: 0.47-0.73, p < 0.001; I2 = 0%) after controlling of potential confounding factors. In addition, pooled results showed that aspirin use was not associated with a significantly increased risk of major bleeding events (RR: 1.42, 95% CI: 0.81-2.51, p = 0.22; I2 = 29%) in patients with HCC. CONCLUSIONS: Evidence from retrospective studies suggests that aspirin use is associated with reduced recurrence and all-cause mortality of HCC. These results should be validated in prospective cohort studies and randomized controlled trials.

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

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.

Proton pump inhibitor use and mortality in patients with cirrhosis: a meta-analysis of cohort studies.

BACKGROUND: Proton pump inhibitor (PPI) is commonly used in patients with cirrhosis. However, some studies demonstrated that PPI use was associated with adverse outcome in patients with cirrhosis. We aimed to perform a meta-analysis of cohort studies to evaluate the association between PPI use and mortality in cirrhotic patients. METHODS: Relevant studies were obtained via search of PubMed and Embase databases. A randomized-effect model was used to pool the results. Subgroup analyses were performed to evaluate the source of heterogeneity. RESULTS: Overall, 21 cohort studies with 20,899 patients and 7457 death events were included. The pooled results with a randomized-effect model showed that PPI use was associated with significantly increased risk of mortality in patients with cirrhosis (adjusted relative risk [RR] = RR: 1.39, P<0.001) with considerable heterogeneity (I2=73%). Subgroup analyses showed that characteristics such as patient ethnicity, sample size, definition of PPI use, and complications of patients did not affect the association. However, the association between PPI use and mortality was independent of study characteristics including patient ethnicity, sample size, complications, definition of PPI use, and follow-up duration. However, the association between PPI use and mortality in cirrhotic patients was significant in retrospective studies (RR: 1.40, P<0.001), but not in prospective studies (RR: 1.34, P=0.33). CONCLUSIONS: PPI use may be associated with moderately increased mortality in cirrhotic patients. Although prospective cohort studies are needed to validate our findings, PPI should only prescribed to cirrhotic patients with indications for the treatment.

Aspirin Use and the Incidence of Hepatocellular Carcinoma in Patients With Hepatitis B Virus or Hepatitis C Virus Infection: A Meta-Analysis of Cohort Studies.

Background: The association between aspirin use and the incidence of hepatocellular carcinoma (HCC) in patients with hepatitis B virus (HBV) or hepatitis C (HCV) virus infection remains not fully determined. A meta-analysis was performed to summarize the findings of cohort studies. Methods: Relevant cohort studies were retrieved via a search of PubMed Cochrane's Library and Embase databases. A random-effect model was used to pool the results. Subgroup analyses were performed to evaluate the influence of study characteristics on the association. Results: Seven cohort studies with 120,945 adult patients with HBV or HCV infection were included. Pooled results showed that aspirin use was independently associated with a reduced risk of HCC in these patients (risk ratio: 0.73, 95% confidence interval: 0.64 to 0.83, p < 0.001; I2 = 86%). Subgroup analyses showed that aspirin use was associated with a reduced HCC risk regardless of the viral type, age, sex, the diabetic, and cirrhotic status of the patients, and the follow-up durations. Moreover, consistent results were obtained in studies with and without adjustment of antiviral treatment and statin use. Pooled results of four studies showed that aspirin use was associated with an increased risk of gastrointestinal bleeding in these patients (risk ratio: 1.15, 95% confidence interval: 1.02 to 1.28, p = 0.02; I2 = 0%). Conclusions: Aspirin use was independently associated with a reduced risk of HCC in patients with HBV or HCV infection, whereas the risk of gastrointestinal bleeding may be increased. These results should be validated in clinical trials.