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.

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.

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.

scholarly journals A Dynamic Integrated Fault Diagnosis Method for Power Transformers

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Wensheng Gao ◽  
Cuifen Bai ◽  
Tong Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Failure Modes ◽  
Selection Process ◽  
Power Transformers ◽  
Diagnosis Method ◽  
One Step ◽  
Diagnosis Model ◽  
Transformer Fault ◽  
Dynamic Fault Diagnosis

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

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.

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