New Methold on Power Transformer Fault Diagnosis

This paper improves the simple genetic algorithm and combines genetic algorithm with BP algorithm to the wavelet neural network in the power transformer fault diagnosis by dissolved gas-in-oil analysis, Simulation result shows the problem was solved that wavelet network settles into local small extremum so easily that the network surging will increase and the network will not be convergent if the initialization is unreasonable, and overcomes the shortcoming that the speed is too slow if use genetic algorithm to train neural network independently.

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An Improved Neural Network Algorithm and its Application in Fault Diagnosis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2355 ◽

2013 ◽

Vol 765-767 ◽

pp. 2355-2358

Author(s):

Tai Shan Yan ◽

Guan Qi Guo ◽

Wu Li ◽

Wei He

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Power Transformer ◽

Convergence Speed ◽

Bp Algorithm ◽

Network Algorithms ◽

Convergence Error ◽

Neural Network Algorithm ◽

Transformer Fault ◽

Improved Bp Algorithm

Aiming at BP neural network algorithms limitation such as falling into local minimum easily and low convergence speed, an improved BP algorithm with two times adaptive adjust of training parameters (TA-BP algorithm) was proposed. Besides the adaptive adjust of training rate and momentum factor, this algorithm can gain appropriate permitted convergence error by adaptive adjust in the course of training. TA-BP algorithm was applied in fault diagnosis of power transformer. A fault diagnosis model for power transformer was founded based on neural network. The illustrational results show that this algorithm is better than traditional BP algorithm in both convergence speed and precision. We can realize a fast and accurate diagnosis for power transformer fault by this algorithm.

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Power transformer fault diagnosis based on dissolved gas analysis by artificial neural network

2012 First International Conference on Renewable Energies and Vehicular Technology ◽

10.1109/revet.2012.6195276 ◽

2012 ◽

Cited By ~ 17

Author(s):

Souahlia Seifeddine ◽

Bacha Khmais ◽

Chaari Abdelkader

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fault Diagnosis ◽

Power Transformer ◽

Gas Analysis ◽

Dissolved Gas ◽

Dissolved Gas Analysis ◽

Artificial Neural ◽

Transformer Fault

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Fault Diagnosis Analysis of Power Transformer Based on PSO-BP Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.466-467.789 ◽

2012 ◽

Vol 466-467 ◽

pp. 789-793

Author(s):

Hui Qin Sun ◽

Zhi Hong Xue ◽

Ke Jun Sun ◽

Su Zhi Wang ◽

Yun Du

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Search Algorithm ◽

Power Transformer ◽

Network Models ◽

Pso Algorithm ◽

Bp Algorithm ◽

Neural Network Models ◽

Bp Network ◽

Transformer Fault

BP neural network is currently the most widely used of neural network models in practical application in transformer fault diagnosis. BP algorithm is a local search algorithm which is easy to make the network into the local minimum values. Network training results are poor. It discusses PSO-BP algorithm which combines the particle swarm optimization (PSO) algorithm with the BP algorithm in this paper. It uses PSO algorithm to optimize the BP network’s weights and threshold. It is used in power transformer fault diagnosis. Experimental data results show that PSO-BP network fault diagnosis accuracy is higher than BP algorithm.

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Power Transformers Fault Diagnosis Based on DRNN

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.960-961.700 ◽

2014 ◽

Vol 960-961 ◽

pp. 700-703

Author(s):

Hui Da Duan ◽

Qiao Song Li

Keyword(s):

Fault Diagnosis ◽

Prediction Error ◽

Power Transformer ◽

Gas Analysis ◽

Bp Algorithm ◽

Power Transformers ◽

Dissolved Gas ◽

Simulation Results ◽

Recursive Prediction ◽

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, diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. To overcome disadvantages of BP algorithm, a new recursive prediction error algorithm (RPE) is used in this paper.In addition, to demonstrate the effectiveness and veracity of the proposed method, some cases are used in the simulation. The simulation results are satisfactory.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.448-453.2520 ◽

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.

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