The insulated gate bipolar transistor (IGBT) is one of the most fragile components in power electronics converters. In order to improve the reliability of IGBTs, various measurements are taken according to the condition monitoring (CM) technique. Traditional CM techniques include the measurement and estimation of the device operation conditions. Recently, emerging techniques have been developed, not only for the detection and estimation but also for the prognostics of IGBTs with the condition data. In this paper, a review is performed on the recent progress in the CM techniques for IGBTs. First, some emerging electrical and thermal measurements are reviewed. Based on the sensed data, the health indicator estimation techniques are summarised. Moreover, for the emerging prognostics and health management applications, some remaining using lifetime (RUL) prediction methods are reviewed. Finally, the research gaps and directions are discussed for the CM in IGBT applications.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%
International projects are often realized through the bidding process, but the existence of information exchange barriers in different countries leads to a complex and tedious bidding process. In this paper, the authors study the complex problem of reading bidding documents, combine the artificial intelligence to analyze them in a structured way, and realize the intelligent analysis of bidding documents. Firstly, through the CRF model, the structured analysis of the tender document is carried out according to the title of the tender document, and the extraction of quoted price, technology and commercial part is realized. Secondly, the detailed analysis of quoted price and technology is completed using the Bi-LSTM method, and the main five key feature extraction and analysis are completed. Finally, based on the CRF-Bi-LSTM method, the actual test is carried out, and the correlation coefficient is as high as 0.965. The results show that the structured parsing model proposed has good application prospects.
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