Fault Diagnosis for the Short-Circuit Fault of the Single-phase Five-Level VIENNA Active Rectifier

Author(s):  
Pham Thi Thuy Linh ◽  
Nguyen Ngoc Bach ◽  
Doan Van Binh
2020 ◽  
Vol 31 ◽  
pp. 101658 ◽  
Author(s):  
Jianwen Meng ◽  
Moussa Boukhnifer ◽  
Claude Delpha ◽  
Demba Diallo

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 934
Author(s):  
Yanwen Wang ◽  
Le Wang ◽  
Sven G. Bilén ◽  
Yan Gao

Due to the working condition of low-voltage cabling from the mining flameproof movable substation to the loads of the mining face being poor, it is easy to cause various external mechanical damages to the cable sheaths. Furthermore, a single-phase earth leakage fault or short-circuit fault can occur when the low-voltage cable sheaths are damaged, and electric sparks caused by these faults can lead to a gas explosion. As the gas detonation time caused by the above faults is usually more than 5 ms, the high-speed interruption solid-state switch which controls the cables must cut off the current within 3 ms. This requires the action time of the solid-state switch to be less than 1 ms, and at the same time, the sampling and calculation time of the relay protection must be less than 2 ms. Based on these problems, this paper proposes the use of a high-speed solid-state circuit breaker (SSCB) topology at the neutral point of transformer, and analyzes the conduction mechanism and shut-off mechanism of the current of the SSCB. It presents an ultra-high-speed algorithm based on pattern recognition of single-phase earth leakage fault protection, and an ultra-high-speed algorithm of short-circuit fault which is based on the rate-of-change of the current. Finally, through computer simulation experiments and semi-physical simulation experiments, the feasibility of the above three technologies is verified to ensure that when a single-phase earth leakage fault or short-circuit fault occurs in the low-voltage cable, the solid-state switch which is installed in the mining flameproof movable substation will cut off the current within 3 ms.


Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3054 ◽  
Author(s):  
Yanling Lv ◽  
Yuting Gao ◽  
Jian Zhang ◽  
Chenmin Deng ◽  
Shiqiang Hou

As a new type of generator, an asynchronized high-voltage generator has the characteristics of an asynchronous generator and high voltage generator. The effect of the loss of an excitation fault for an asynchronized high-voltage generator and its fault diagnosis technique are still in the research stage. Firstly, a finite element model of the asynchronized high-voltage generator considering the field-circuit-movement coupling is established. Secondly, the three phase short-circuit loss of excitation fault, three phase open-circuit loss of excitation fault, and three phase short-circuit fault on the stator side are analyzed by the simulation method that is applied abroad at present. The fault phenomenon under the stator three phase short-circuit fault is similar to that under the three phase short-circuit loss of excitation. Then, a symmetrical loss of the excitation fault diagnosis system based on wavelet packet analysis and the Back Propagation neural network (BP neural network) is established. At last, we confirm that this system can eliminate the interference of the stator three phase short-circuit fault, accurately diagnose the symmetrical loss of the excitation fault, and judge the type of symmetrical loss of the excitation fault. It saves time to find the fault cause and improves the stability of system operation.


2019 ◽  
Vol 9 (2) ◽  
pp. 224 ◽  
Author(s):  
Siyuan Liang ◽  
Yong Chen ◽  
Hong Liang ◽  
Xu Li

Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes a fault diagnosis method based on sparse representation and support vector machine (SVM). Firstly, the sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and then they are composed into four-dimensional feature vectors. Secondly, the feature vectors are input into the support vector machine for fault diagnosis, which is suitable for small sample. Experiments on a permanent magnet synchronous motor with artificially set inter-turn short-circuit fault and a normal one showed that the method is feasible and accurate.


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