Transmission network fault location using sparse PMU measurements

A new method of fault diagnosis for power networks by using the combinatorial cross entropy (CCE) algorithm is proposed. The research contents in this paper mainly contain the two parts: transmission network fault diagnosis and distribution network fault location. For transmission network, the optimization model is built by considering the relationship among fault elements, action information of protective relays and circuit breakers. For distribution network, constructing fault location model according to the logic relationship between fault current and equipment condition. The optimal solution of two models are solved by CCE algorithm, then fault element (s) in transmission network and fault section (s) in distribution network can be identified by the optimal solution. Various fault conditions are simulated in test system and the results show that conclusions obtained by proposed method are correct, which prove CCE algorithm diagnose fault effectively.

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Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

The Open Mechanical Engineering Journal ◽

10.2174/1874155x01408010916 ◽

2014 ◽

Vol 8 (1) ◽

pp. 916-921

Author(s):

Yuan Yuan ◽

Wenjun Meng ◽

Xiaoxia Sun

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Fault Location ◽

Fuzzy Neural Network ◽

Fuzzy Theory ◽

Belt Conveyor ◽

Fuzzy Neural ◽

Neural Network Algorithm ◽

Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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A Remote Educational Video Dynamic Image Sharp Processing Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.760-762.1562 ◽

2013 ◽

Vol 760-762 ◽

pp. 1562-1566

Author(s):

Qian Jun Tang ◽

Yan Zhang

Keyword(s):

Distance Education ◽

Fault Detection ◽

Transmission Efficiency ◽

Video Image ◽

Transmission Network ◽

Characteristic Analysis ◽

Remote Education ◽

Transmission Distance ◽

Education Network ◽

Network Fault

In distance education network transmission process, because transmission distance is too long, transmission network will be affected by complicated external environment factors, which leads to network failure and failure in remote education video image formation, and finally causes unsmooth transmission. This paper puts forward a distributed network fault detection technology to perform fault detection for remote education transmission network nodes and characteristic analysis of the use of network fault by using genetic neural network, accurately locate fault node area so as to realize the remote education networks fault detection. Experiments show that this method can avoid distance education network fault resulted from long transmission distance and improve the transmission efficiency of remote education video image.

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