154 research outputs found

    Smartphone Camera Based Visible Light Communication

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    The paper proposes a novel camera-based receiver for visible light communications for a short range mobile-to-mobile communications link. The receiver captures data from the screen of a transmitting smartphone and uses the speeded up robust features algorithm to effectively detect it. The receiver performs a projective transformation to accurately eliminate perspective distortions caused by the displacement of the devices. The paper also introduces a quantization process in order to suppress the inter-symbol interference resulting from the dynamic nature of the environment. A range of experiments are carried out in order to evaluate the system performance when the position parameters are varied. We show that the proposed system is capable of achieving a very high success rate of 98% in recovering the transmitted images under test conditions

    Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission

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    Copyright: © 2022 by the authors. 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 extrac-tion. Then, two classification algorithms—multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)—are used for classifi-cation. 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 re-sults.National Natural Science Foundation of China, grant no. 51105291; by the Shaanxi Provincial Science and Technology Agency, nos. 2020GY124, 2019GY-125, and 2018JQ5127; Key Laboratory Project of the Department of Education of Shaanxi Province, nos. 19JS034 and 18JS045

    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

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    Copyright: © 2021 by the authors. Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To classify directly 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.National Natural Science Foundation of China; Shaanxi Provincial Science and Technology Agency; Key Laboratory Project of Department of Education of Shaanxi Provinc
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