Fault Diagnosis of Transformer Based on RBF Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
High Energy ◽  
Ratio Method ◽  
Matlab Simulation ◽  
Medium Temperature ◽  
The Neural Network ◽  
Transformer Fault ◽  
Network Diagnosis

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.

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.

scholarly journals Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yu Ding ◽  
Qiang Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Kriging Model ◽  
Data Driven ◽  
Power Transformers ◽  
Global Approximation ◽  
The Neural Network ◽  
Modeling Accuracy ◽  
Diagnosis Method ◽  
Transformer Fault

A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method. The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy. Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.

Diagnosis Technique Based on BP and D-S Theory

2011 ◽  
Vol 179-180 ◽  
pp. 544-548
Author(s):  
Qiu Yun Mo ◽  
Jie Cao ◽  
Feng Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
The Neural Network ◽  
Evidential Theory ◽  
The Given ◽  
Fusion Framework ◽  
Level Fusion ◽  
Theory And Experiments

This paper constructs a common data fusion framework of fault diagnosis, by combining local neural networks with dempster-shafer (D-S) evidential theory. The RBF neural network is proposed as a local neural network of the fault pattern recognition, and its input vectors are extracted by the wavelet packet decomposition of various frequency energy. Then, the signal of each sensor separately has a feature level fusion. This method is effective, verified by experiments. The given decision level fusion is based on combining the features of the neural network and the D-S theory, and experiments show the results of the fault diagnosis are more accurate by this method.

Power Transformers Fault Diagnosis Based on Fuzzy-RBF Neural Network

2012 ◽  
Vol 614-615 ◽  
pp. 1303-1306 ◽  
Author(s):  
Hui Da Duan ◽  
Xin Yao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Power Transformers ◽  
The Neural Network ◽  
Gradient Descent Algorithm ◽  
Neural Network Input

Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. Improved three-ratio is an effective method for transformer fault diagnosis used in recent years. This paper applies appropriate Artificial Neural Networks (ANN) to resolve the online fault diagnosis problems for oil-filled power transformer based on improved three-ratio. Because of the characteristic of improved three-ratio boundary is too absolute, a method using fuzzy math theory to deal with the data of the neural network input is also proposed. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure. In addition, to improve the convergence speed, an improved gradient descent algorithm is used in training RBFNN. Through on-line monitoring the concentrations of the dissolved gases, the proposed diagnostic system can offer a way to interpret the incipient faults. The simulation diagnosis demonstrates the effectiveness and veracity of the proposed method.

The Engine Fault Diagnosis with RBFNN Based on AIC

2012 ◽  
Vol 468-471 ◽  
pp. 1066-1069
Author(s):  
Qiang Huang ◽  
Xiao Zhuo Ouyang ◽  
Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Rbf Neural Network ◽  
Information Criterion ◽  
Vibration Signals ◽  
The Neural Network ◽  
Rotary Machines ◽  
Diagnosis Method ◽  
Diagnosis Model

In this paper, an engine diagnosis method with high precision and quickly response is proposed. Firstly, the Akaike Information Criterion (AIC) is used to improve the performance of the neural network to build the fault diagnosis model. Then the vibration signals are analyzed to estimate the states of the diesel engine. Finally, the five states of diesel engine are set to validate the veracity of diagnosis method. According to experiment and simulation researches, it indicates that the diagnosis method with RBF neural network based on AIC is effective. The veracity of identification is 100% to the single fault. It is a valuable reference to the vibration diagnosis for other complex rotary machines.

Oil-Filled Power Transformers Fault Diagnosis Based on Fuzzy-DRNN

2013 ◽  
Vol 448-453 ◽  
pp. 2520-2523
Author(s):  
Ying Ping Fan ◽  
Hui Da Duan
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Dynamic Neural Network ◽  
The Neural Network ◽  
Transformer Fault

In recent years, improved three-ratio is an effective method for transformer fault diagnosis based on Dissolved Gas Analysis (DGA). In this paper, a simple dynamic neural network named as diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. Because of the characteristic of improved three-ratio boundary is lack of matching, fuzzy logic in fault diagnosis is presented also to deal with the data of the neural network inputs. DRNN is used to model the fault diagnosis structure, the fuzzy logic is used to improve the faults diagnose reliability. In addition, some cases are used to show the capability of the suggested method in oil-filled power transformers fault diagnosis.

New Method of Diagnosis and New Knowledge Acquirement for Fault Diagnosis Expert System of High Voltage Circuit Breaker

2012 ◽  
Vol 466-467 ◽  
pp. 1413-1417
Author(s):  
Yong Mei Yang ◽  
Nai Quan Sun ◽  
Hong Ke Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Rbf Neural Network ◽  
Circuit Breaker ◽  
Forward Reasoning ◽  
The Neural Network ◽  
New Knowledge ◽  
On Line ◽  
Improved Algorithm

In this paper, an improved algorithm of general radial basis (RBF) function neural network is introduced, based on improved algorithm, the neural network realized quickly fault diagnosis and self-update of neural network structure, and the neural network is applied to the on-line fault diagnosis expert system. The expert system deals with the fault data that send from on-line monitoring equipment by using neural network, and it can discover the fault type and give reasonable solution by forward reasoning. Meanwhile, the expert system has the ability of achieving new knowledge based on the application of self-update ability of RBF neural network.

scholarly journals The Application of RBF Neural Network Optimized by K-means and Genetic-backpropagation in Fault Diagnosis of Power Transformer

2021 ◽  
Vol 242 ◽  
pp. 03002
Author(s):  
Xinxin Mi ◽  
Gopinath Subramani ◽  
Mieowkee Chan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Training Data ◽  
Transformer Fault

Through the dissolved gas analysis (DGA) in transformer oil, the fault of the power transformer can be diagnosed. However, the DGA method has the disadvantage of low accuracy because it couldn’t exactly reflect the nonlinear relationship between the characteristic gases and fault types. Radial basis function neural network (RBFNN) has the advantage of dealing with complex nonlinear problems, so it can be applied to transformer fault diagnosis based on DGA. The centers, widths and weights has important effects on the performance of the RBFNN. However, it is difficult to find the global optimal solution of these parameters when RBFNN training. This paper creatively designs a method to improve these parameters of RBFNN, firstly using the K-means algorithm to optimize the centers and widths of RBFNN, then using the genetic algorithm-backpropagation (GA-BP) algorithm optimize the weights. Finally, establish the K-means RBF-genetic backpropagation (KRBF-GBP) algorithm model through a large amount of training data. The test results show that the fault diagnosis accuracy of the KRBF-GBP algorithm is 96.4%, higher than the unoptimized RBFNN with 71.43%.

Garbage Crusher Fault Diagnosis Based on RBF Neural Network

2009 ◽  
Vol 16-19 ◽  
pp. 971-975
Author(s):  
Yong Hou Sun ◽  
Cong Li ◽  
Mei Fa Huang ◽  
Hui Jing
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Structure And Properties ◽  
Automatic Equipment ◽  
Rbf Network ◽  
The Neural Network ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Training Simulation

The garbage crusher is a new kind of crusher for garbage crushing when processing Municipal Solid Waste (MSW). With the development of automatic equipment and the complication of structure and properties of the garbage crusher, the fault diagnosis of garbage crusher is very important. In this paper, according to the fault symptoms and parameters, Radial Basis Function Neural Network (RBF NN) is used for fault diagnosis of the garbage crusher. The structure and inference of RBF NN are discussed in detail. The garbage crusher fault diagnosis model is established based on RBF network. At last, the fault of mechanical system is taken as an example of garbage crusher fault diagnosis. Training simulation results of the neural network are given base on MATLAB software. The result shows the RBF NN is suitable for fault diagnosis of garbage crusher.

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