Power Cable Fault Recognition and Location Using Phase-Mode and Wavelet

2016 ◽  
Author(s):  
Changfeng Xu ◽  
Mei Wang ◽  
Pai Wang ◽  
Xuebin Qin ◽  
Liang Wang
Keyword(s):  
Power Cable ◽  
Fault Recognition

Cable Fault Recognition Based on the Wavelet Energy Spectrum of Mode Components

2013 ◽  
Vol 427-429 ◽  
pp. 834-837
Author(s):  
Mei Wang ◽  
Jing Wu
Keyword(s):  
Energy Spectrum ◽  
High Frequency ◽  
Transient Current ◽  
Power Cable ◽  
Two Phase ◽  
High Frequencies ◽  
Wavelet Energy ◽  
Fault Recognition ◽  
Frequency Components ◽  
Independent Mode

When a fault appeared in a power cable transmission line, the transient current with high frequencies would be produced in the system. Three independent mode components could be obtained by applying the phase mode transformation to the transient current. For different types of the faults, the three independent mode components have different features. Based on wavelet energy spectrum of mode components, a method for cable fault recognition is developed in this paper. First, the fault current is decomposed by using Karenbaue transformation matrix. Then, wavelet transformation is uses to obtain the coefficients of the high frequency components which reflect the original signal high frequency energy. Finally, based on the wavelet energy spectrum method and the detailed coefficient manipulation, the equivalent norms of the mode components are obtained. Compared with the traditional fault recognition method, the new method depends less on zero mode component in two-phase short to ground state, and it can recognize the fault class in the cases of different fault positions, different fault path resistances and different inception angles.

scholarly journals Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

Algorithms ◽  
2014 ◽  
Vol 7 (4) ◽  
pp. 492-509 ◽  
Author(s):  
Xuebin Qin ◽  
Mei Wang ◽  
Jzau-Sheng Lin ◽  
Xiaowei Li
Keyword(s):  
Competitive Learning ◽  
Power Cable ◽  
Learning Network ◽  
Fault Recognition

Power Cable Fault Recognition Using the Improved PSO-SVM Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 830-833 ◽  
Author(s):  
Mei Wang ◽  
Xiao Wei Li
Keyword(s):  
Function Parameter ◽  
Support Vector ◽  
Power Cable ◽  
Power Cables ◽  
Simulation Experiments ◽  
Training Time ◽  
Fault Recognition ◽  
Svm Algorithm ◽  
Improved Pso

Power cables are increasingly popular in daily life and industrial production. The long-term use will make various cable faults. To reduce the losses caused by the faults, the cable faults should be recognized correctly and timely. In this paper, we developed an improved particle swarm optimization and support vector machine (IPSO-SVM) algorithm to recognize the power cable faults. The algorithm used the improved PSO to optimize the SVM kernel function parameter and the penalty parameter simultaneously. Two advantages were illustrated by the simulation experiments. The first one is the recognition accuracy which was increased from 81.8% to 90.9%; the second advantage is the SVM training time which decreased from 0.0247 second to 0.0202 second.

Research on Enhancement Technology of Conveyor Belt Fault Images Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 685-689
Author(s):  
Chunqing Ye ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Yanli Yang
Keyword(s):  
Optimization Algorithm ◽  
Detection System ◽  
Fruit Fly ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wire Ropes ◽  
Fault Recognition ◽  
Steel Cord

In this research, we studied the fault recognition algorithm of steel cord conveyor belt, and obtained the wire ropes image by adopting the detection system of steel cord conveyor belt, so that the fault recognition algorithm of steel cord conveyor belt was proposed based on Fruit fly optimization algorithm. As we know that the fruit fly optimization algorithm is used for fault detection of the processing steel cord conveyor belt image and for obtaining the fault image. In the MATLAB environment, the algorithm process was designed and verified in terms of the effectiveness and accuracy. The experimental results show that with fast speed and high accuracy in detecting the fault image of steel cord conveyor belt rapidly and accurately, and in classifying scratch from fracture the proposed algorithm is suitable for the fault recognition of steel cord conveyor belt automatically.

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