Fault Diagnosis of LLC Converter Controlled by Fractional Order $PI^{\lambda}D^{\mu}$ under Fault Tree

Li, Ming; Lu, Jianning

doi:10.1109/iciibms50712.2020.9336201

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Yaxin Li et al proposed an improved convolutional neural network which uses global average pooling instead of the traditional fully connected layers. The improved model has high diagnostic accuracy, high generalization capability and low computational burden under small sample conditions [20]. Yang Guangyou et al proposed a SA-ACGAN-BP method for bearing fault diagnosis with unbalanced samples, which reduces the workload and difficulty of tuning the parameters by measuring the relative performance between the generator and the discriminator and dynamically adjusting the balance between them to make the convergence more stable and rapid.…”

Section: Deep Learning-based Fault Diagnosis Methodsmentioning

confidence: 99%

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

Tong

Wang

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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The structure of wind turbine is complicated and the operating environment is harsh, which leads to the high probability of failure of wind turbine, and the maintenance is more difficult. Once an accident occurs, it will force the wind turbine to shut down, causing huge economic losses. In addition, it will affect the safe and reliable operation of the whole power system. Therefore, fault diagnosis of wind power generation system is very important. The traditional fault diagnosis algorithm can only identify the known types of faults in wind turbines. If a new fault category appears in the wind turbine generator, the traditional fault diagnosis algorithm can only identify it as a known fault category. To solve this problem, a new classification fault diagnosis method based on semi-supervised deep learning is proposed. The benchmark model of offshore wind turbine is built in FAST software, and 10 different types of faults such as sensors and systems are simulated through model simulation. Different responses of fault signals are analyzed, and 15 parameter signals are selected as input. Firstly, fault features are extracted through multi-scale convolutional self-coding network. Secondly, the initial model is established by using the compressed feature and the error feature map as the input of the classifier and the detector respectively. Finally, the detector judges and puts the new category of fault instances into the buffer. When the buffer reaches the maximum value and starts to overflow, the algorithm starts to update, so as to realize the diagnosis task of new types of faults. Compared with the experimental results of isolated forest, local anomaly factor and single-class support vector machine, the accuracy of our proposed method and other indicators are significantly better, with an accuracy of 99.2%, which can effectively solve the problem of new class fault identification of wind turbines. It can effectively solve the problem of fault identification of the new type of fan, and is conducive to avoiding the occurrence of shutdown accidents, thus maintaining the safe and reliable operation of the power system.

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Section: Deep Learning-based Fault Diagnosis Methodsmentioning

confidence: 99%

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

Tong

Wang

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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“…Moreover some works have been concerned with the implementation of fractional order PID (FO-PID) controllers in efficient fault tolerant control structures [81,82,83], for example in [84,85] the authors were able to showcase the superior performances of such controllers compared to their integer order counterparts in dealing with unexpected faults, and in [86] the design of a FOPID controller combined with a fractional active disturbance rejection controller was able to provide satisfactory control and robustness for reentry flight control of hyper-sonic aircraft under actuator faults. In the mean time, in [87] a so-called Tilt Integral Derivative (TID) which is a type of FOPID controller where the Proportional action is replaced by Fractional integrator of order 1/n was proposed and successfully implemented for the level control of a two tanks system in the presence of actuator and components faults; whereas in [88] a FOPID controller along with a Tilt PID T-PID controller were design for the voltage control of a self-excited induction generator subjected to voltage sensor faults.…”

Section: Pid-based Ftcmentioning

confidence: 99%

Fractional Order Fault Tolerant Control - A Survey

Ladaci,

Benchaita

2023

IJRCS

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In this paper, a comprehensive review of recent advances and trends regarding Fractional Order Fault Tolerant Control (FOFTC) design is presented. This novel robust control approach has been emerging in the last decade and is still gathering great research efforts mainly because of its promising results and outcomes. The purpose of this study is to provide a useful overview for researchers interested in developing this interesting solution for plants that are subject to faults and disturbances with an obligation for a maintained performance level. Throughout the paper, the various works related to FOFTC in literature are categorized first by considering their research objective between fault detection with diagnosis and fault tolerance with accommodation, and second by considering the nature of the studied plants depending on whether they are modelized by integer order or fractional order models. One of the main drawbacks of these approaches lies in the increase in complexity associated with introducing the fractional operators, their approximation and especially during the stability analysis. A discussion on the main disadvantages and challenges that face this novel fractional order robust control research field is given in conjunction with motivations for its future development. This study provides a simulation example for the application of a FOFTC against actuator faults in a Boeing 747 civil transport aircraft is provided to illustrate the efficiency of such robust control strategies.

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Fault Diagnosis of LLC Converter Controlled by Fractional Order $PI^{\lambda}D^{\mu}$ under Fault Tree

Cited by 2 publications

References 6 publications

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

A new class of fault diagnosis method for wind turbines based on multiscale convolutional neural networks

Fractional Order Fault Tolerant Control - A Survey

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