Seismic performance analysis of double electrical equipment system connected by flexible conductors

Structural characteristics of electrical equipment are influenced by connected flexible conductors. In order to study the seismic performance of the double electrical equipment system (DEES) with flexible conductors, a simplified modeling method of the DEES with flexible conductors was proposed in this paper. The refined finite element model of the DEES with flexible conductors was established using ANSYS software. Its seismic response level was analyzed and compared with that of the standalone equipment. Besides, the impact of ground motion input was also investigated. The results show that the first two modal frequencies of the DEES will be reduced by the flexible conductor. The flexible conductor can reduce the seismic responses of the low-frequency equipment in the DEES, while it increases seismic responses of the high-frequency equipment. When the slack of the flexible busbar is greater than a critical value, the additional pulling force on the top of the equipment caused by the busbar can be kept at a low level, which is also related to the ground motion input

Automatic Detection of Display Defects for Smart Meters based on Deep Learning

The smart meter is an essential part of an intelligent grid system. Defects in the LCD screen the smart meters affect their use. Therefore, detection of LCD screen defects of smart meters is of great significance for management and use of smart electricity meters. At present, detection methods are mainly realized by manual detection and automatic detection based on machine vision. However, performance of these two methods is not satisfactory. The fault detection task of a smart meter LCD screen can be divided into two parts: smart meter LCD localization and LCD fault detection. Therefore, this paper proposes a twostage system based on deep learning, which combines YOLOv5 with ResNet34. YOLOv5 is used for smart meter LCD localization and the classification network based on ResNet34 for LCD fault detection. We have constructed an LCD screen localization dataset and an LCD screen defect detection dataset to train and test our model. As a result, our model achieves a defect detection accuracy of 98.9% on the dataset proposed in this paper and can accurately detect the common defects of an LCD screen

Ibrutinib versus bendamustine plus rituximab for first-line treatment of 65 or older patients with untreated chronic lymphocytic leukemia without del(17p)/TP53 mutation in China: a lifetime economic research study

Abstract Background The incidence and mortality rates of patients with chronic lymphocytic leukemia (CLL) in China have recently increased. This study performed a long-term economic evaluation of the first-line treatment strategies ibrutinib (IB) or bendamustine (BE) plus rituximab (RI) for previously untreated older patients with CLL without the del(17p)/TP53 mutation in China. Methods Based on clinical data from large, randomized trials, a Markov model including four disease states (event-free survival, treatment failure, post-treatment failure, and death) was used to estimate the incremental costs per quality adjusted-life year (QALY) gained from the first-line IB strategy versus the BE plus RI strategy over a 10-year period. All costs were adjusted to 2022 values based on the Chinese Consumer Price Index, and all costs and health outcomes were discounted at an annual rate of 5%. Sensitivity analysis was performed to confirm the robustness of base-case results. Results Compared to the first-line BE plus RI strategy, first-line IB treatment achieved 1.17 additional QALYs, but was accompanied by $88,046.78 (estimated in 2022 US dollars) in decremental costs per patient over 10 years. Thus, first-line treatment with IB appeared to have absolute dominance compared to the BE plus RI strategy. Sensitivity analysis confirmed the robustness of these results. Conclusions The first-line treatment with IB is absolutely cost-effective compared to the first-line BE plus RI treatment strategy for 65 or older patients with CLL without the del (17p)/TP53 mutation from the Chinese payer perspective. Therefore, it is strongly recommended that Chinese health authorities select the former strategy for these CLL patients

Transformer fault classification for diagnosis based on DGA and deep belief network

Power transformer plays a very important role in power system, its long-term operation will cause various kinds of faults. Accurate identification and timely elimination of transformer faults are the basis of safe operation of power grid. As one of the most commonly used fault diagnosis methods, dissolved gas analysis (DGA) technology is used to identify fault types through dissolved gas in transformer oil, and its reliability has been proved. In order to analyze these gases and diagnose transformer fault types with the results, many methods have been developed, such as Key Gas Method, Method of Duval, IEC 60599 Method, Method of Dornenburg and Method of Rogers, etc. In some cases, the accuracy of these traditional methods is reduced and cannot be applied for diagnosis, since they have fixed input features and is not flexible for input combination. In order to achieve the propose of solving this defect, this paper introduces a deep belief network-based DGA method to diagnose the faults and states of power transformers with customized input features. For this work, six fault classifications were considered based on the nine characteristics extracted from the gases precipitated from the insulating oil of power transformers. The deep belief network was tested using oil samples collected from power transformers. Experiments have shown that the performance of the network has obtained relatively good accuracy results

Outlier detection and data filling based on KNN and LOF for power transformer operation data classification

The missing and abnormal data in power transformer operation and monitoring greatly affect the accuracy of fault diagnosis and thus threaten the stable operation of power systems. To conduct outlier detection and improve data quality for safety warning, this paper proposes a transformer operation data preprocessing method based on KNN (K-nearest neighbor) and LOF (local outlier factor) for power transformer operation data classification. Firstly, this paper analyzes the characteristics of transformer operation data. Secondly, the local reachable density of the input data is calculated by LOF algorithm. The local outlier factor score of the data is derived according to the local reachable density, and the abnormal data is output according to the abnormal score. Then, KNN algorithm is utilized to classify the relevant data around the abnormal value and missing value of the transformer. The data are filled or corrected according to the classification results. Thirdly, the elbow method is used to determine the optimal K value and cluster operation data by K-Means algorithm. Finally, the proposed method is applied and verified with real transformer operation data in case study. The results show the method can effectively detect and correct the abnormal and missing data, conduct transformer data cleaning and preprocessing and provide accurate and effective data samples for transformer fault diagnosis