Defects Identification of Partial Discharge Map in GIS

2013 ◽  
Vol 448-453 ◽  
pp. 1947-1950
Author(s):  
Yi Long Zhang ◽  
Yi Hui Zheng ◽  
Li Xue Li ◽  
Xin Wang ◽  
Gang Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Random Function ◽  
Rbf Neural Network ◽  
Partial Discharge ◽  
Rbf Network ◽  
Novel Method ◽  
Training Error ◽  
Defect Pattern ◽  
Defects Identification

With GIS being widely used, partial discharge detecting and defect pattern recognition become more and more meaningful and important. To realize defects identification of partial discharge map in GIS, a novel method based on Radical Basis Function (RBF) neural network is proposed. Firstly, a model is constructed to simulate the discharge pattern map by the use of random function randint. Secondly, based on the model above, a lot of data which meet the condition can be collected to provide for pattern recognition. Then, a RBF network is introduced to identify the pattern recognition. It can be trained by using the data above. Finally, through changing training error, high correct rate can be got. These indicate that the method is effective.

An Improved Direct Neural Network Approach to Flatness Pattern Recognition Baseed on GA-RBF

2011 ◽  
Vol 383-390 ◽  
pp. 2958-2962 ◽  
Author(s):  
Xiao Hua Feng ◽  
Xiao Juan Sun
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Rbf Neural Network ◽  
Neural Network Approach ◽  
New Approach ◽  
Rbf Network ◽  
Fast Learning ◽  
Flatness Pattern Recognition ◽  
Direct Recognition ◽  
Speed And Accuracy

In this paper, an improved the radial basis function (RBF) neural network direct recognition approach to shape flatness pattern is proposed. The genetic algorithm (GA) is employed to obtain more optimal structure and initial parameters of RBF network. The new approach with the advantages of RBF, such as fast learning and high accuracy, is efficient and intelligent. it can not only effectively settle the problem of the different topologic configurations with changing strip widths but also improve practicability and precision.Compared to the improved direct recognition method with GA-BP,The simulation results show that the speed and accuracy of the flatness pattern recognition model based on GA-RBF are obviously improved.

Comparison between BP and RBF Neural Network Pattern Recognition Process Applied in the Droplet Analyzer

2014 ◽  
Vol 543-547 ◽  
pp. 2333-2336
Author(s):  
Qing Song ◽  
Gao Jie Meng ◽  
Lu Yang ◽  
Dan Qing Du ◽  
Xue Fei Mao
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Liquid Droplet ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Uncertain System ◽  
Training Data ◽  
Waveform Analysis ◽  
Bp Network ◽  
Rbf Network

Among various pattern recognition methods used for liquid identification, the method based on neural network has the advantages of robustness and fault tolerance, which can study and adapt to the uncertain system. The waveform analysis is exploited for feature extraction of the liquid droplet fingerprint (LDF) in this paper, and the liquid identification is carried out by means of BP and RBF neural network. The experimental results proved that the recognition rate is excellent in both of these two methods. In condition that the training data is limited, RBF network is better than BP network in recognition speed and rate.

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems

2012 ◽  
Vol 460 ◽  
pp. 127-130
Author(s):  
Song He Zhang ◽  
Yue Gang Luo ◽  
Bin Wu ◽  
Bing Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Amplitude Spectrum ◽  
Rates Of Convergence ◽  
Rotor System ◽  
Rbf Network ◽  
Characteristic Quantity ◽  
Learning Rates ◽  
Fault Characteristic

The RBF network was applied in the rotor system to realize the fault diagnosis aiming the mapping complexity between fault symptoms and fault patterns. It can overcome the problems of low learning rates of convergence and falling easily into part minimums in BP algorithm, and improve the precision of diagnosis. The normalized values of seven frequency ranges in amplitude spectrum were used as the fault characteristic quantity, the RBF network was trained to diagnose the faults of rotor system. The results show that RBF neural network is a valid method of diagnosis of mechanical failure.

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

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