Fault identification scheme for UPFC compensated transmission line based on characteristic voltage active injection

Author(s):  
Tao Zheng ◽  
Yunpeng Wang ◽  
Wenxuan Lv ◽  
Zuowei Wang ◽  
Yongxin Chen ◽  
...  
2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Bo Zhao ◽  
Yuanchun Li

This paper concerns with a fault identification scheme in a class of nonlinear interconnected systems. The decentralized sliding mode observer is recruited for the investigation of position sensor fault or velocity sensor fault. First, a decentralized neural network controller is proposed for the system under fault-free state. The diffeomorphism theory is utilized to construct a nonlinear transformation for subsystem structure. A simple filter is implemented to convert the sensor fault into pseudo-actuator fault scenario. The decentralized sliding mode observer is then presented for multisensor fault identification of reconfigurable manipulators based on Lyapunov stable theory. Finally, two 2-DOF reconfigurable manipulators with different configurations are employed to verify the effectiveness of the proposed scheme in numerical simulation. The results demonstrate that one joint’s fault does not affect other joints and the sensor fault can be identified precisely by the proposed decentralized sliding mode observer.


2021 ◽  
Author(s):  
Jia Guo ◽  
Jinghai Xie ◽  
Jingzhong Yuan ◽  
Yu Jiang ◽  
Shihua Lu

2014 ◽  
Vol 984-985 ◽  
pp. 996-1004
Author(s):  
D. Miruthula ◽  
Ramachandran Rajeswari

This paper presents a new method to classify transmission line shunt faults and determine the fault location using phasor data of the transmission system. Most algorithms employed for analyzing fault data require that the fault type to be classified. The older fault-type classification algorithms are inefficient because they are not effective under certain operating conditions of the power system and may not be able to accurately select the faulted transmission line if the same fault recorder monitors multiple lines. An intelligent techniques described in this paper is used to precisely detect all ten types of shunt faults that may occur in an electric power transmission system (double-circuit transmission lines) with the help of data obtained from phasor measurement unit. This method is virtually independent of the mutual coupling effect caused by the adjacent parallel circuit and insensitive to the variation of source impedance. Thousands of fault simulations by MATLAB have proved the accuracy and effectiveness of the proposed algorithm. This paper includes the analysis of fault identification techniques using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System based protection schemes. The performances of the techniques are examined for different faults on the parallel transmission line and compared with the conventional relay scheme. The results obtained shows that ANFIS based fault identification gives better performance than other techniques.


2019 ◽  
Vol 13 (15) ◽  
pp. 3252-3263 ◽  
Author(s):  
Mohamed I. Zaki ◽  
Ragab A. El Sehiemy ◽  
Ghada M. Amer ◽  
Fathy M. Abo El Enin

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