A Novel Detection and Localization Approach of Open-Circuit Switch Fault for the Grid-Connected Modular Multilevel Converter

Jin, Yu; Xiao, Qian; Jia, Hongjie; Ji, Yanchao; Dragičević, Tomislav; Teodorescu, Remus; Blaabjerg, Frede

doi:10.1109/tie.2022.3153810

Cited by 31 publications

(11 citation statements)

References 29 publications

(49 reference statements)

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“…It is believed that any error exceeding this interval is not a random error but a gross error. The data containing gross errors should be removed [5]. As shown in Figure 2…”

Section: Outlier Processingmentioning

confidence: 99%

Analysis and Comparison of Loan Default Prediction Models Based on XGBoost and LightGBM Algorithm

2023

AJCIS

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Based on the default loans caused by information asymmetry and uncontrollable factors at this stage, this paper will use two algorithm models, XGBoost and LightGBM, to extract and screen the relevant information of the applicant and build a loan default prediction model to predict the default situation of the loan. And the two different models were compared and evaluated to provide data reference for financial institutions to select and build the loan default prediction model to reduce their risks and bank losses to a certain extent.

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“…It is believed that any error exceeding this interval is not a random error but a gross error. The data containing gross errors should be removed [5]. As shown in Figure 2…”

Section: Outlier Processingmentioning

confidence: 99%

Analysis and Comparison of Loan Default Prediction Models Based on XGBoost and LightGBM Algorithm

2023

AJCIS

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“…The electronic laboratory used Modelica language to design two discrete hybrid simulation models, but the components of the electronic experiment are all two-dimensional planar basic structures. Literature [4] proposed a specific method to build a virtual software electronics laboratory and a remote embedded system recovery system with the help of augmented reality technology. This specific method has been practically applied in electronic information technology professional courses.…”

Section: Introductionmentioning

confidence: 99%

Remote laboratory implementation analysis based on virtual visual simulation

Zhang,

Xin

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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With the rise of network informatization in assisting teaching, students in schools use various informatization methods for teaching. Students do not need to go to school to attend classes and participate in experimental learning activities through the Human-Computer Interaction Learning Center and "Remote Electronic Laboratory". The rapid development of online teaching should require the design of simulation tools, software and facilities for course content and experimental environments, but these key factors have become one of the bottlenecks affecting the quality of online teaching. This paper uses the neural network algorithm in machine learning and artificial intelligence to conduct intensive training and learning on the output and reception signal samples of semiconductor devices to obtain virtual visualization simulation results. Fit the simple data of the received signal to a system of two-dimensional linear equations to obtain the different magnitudes of the signals and obtain accurate coordinate positions. With the help of machine learning visualization processing, model changes can be compared, and the output effects are reasonable and very accurate. Compared with traditional semiconductor devices that do not have visual interface operations, virtual simulation in remote laboratories further expands the scope of use. The GUI program of the virtual visual simulation model created in this article makes the simulation of electronic circuit models simpler and more convenient, and has extensive promotion and reference effects.

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“…Fault detection plays a pivotal role in ensuring the reliable operation of modular multilevel converters (MMCs). With the increasing utilization of MMCs in safety-critical applications such as renewable energy [1], motor drive [2], and power transmissions [3], the failure of MMCs has become increasingly severe. Timely and robust fault detection is vital to mitigate these risks and ensure the continued reliability of these systems.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Informed Threshold Assessment of Model-Based Fault Detection for Modular Multilevel Converters

Liao,

Zhang,

You

et al. 2024

IEEE Open J. Power Electron.

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Determining threshold values in model based fault detection (MBFD) is a longstanding challenge, which is often addressed through empirical and ambiguous adjustments. To tackle this issue, this paper proposes an uncertainty-informed framework for quantitative threshold assessment. The framework comprises three stages: 1) identifying uncertainties by explicitly understanding the implemented MBFD method, 2) quantifying fault detection residual through uncertainty propagation, and 3) determining and optimizing threshold values based on the quantified misdiagnosis rates. To validate the effectiveness of the proposed approach, a case study of a modular multilevel converter is selected. The proposed method not only enables a quantified threshold assessment but also enhances the robustness of the fault detection by accounting for uncertainties.

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A Novel Detection and Localization Approach of Open-Circuit Switch Fault for the Grid-Connected Modular Multilevel Converter

Cited by 31 publications

References 29 publications

Analysis and Comparison of Loan Default Prediction Models Based on XGBoost and LightGBM Algorithm

Analysis and Comparison of Loan Default Prediction Models Based on XGBoost and LightGBM Algorithm

Remote laboratory implementation analysis based on virtual visual simulation

Uncertainty-Informed Threshold Assessment of Model-Based Fault Detection for Modular Multilevel Converters

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