Research on Method of State Evaluation and Fault Analysis of Dry-Type Power Transformer Based on Self-Organizing Neural Network

2013 ◽  
Vol 303-306 ◽  
pp. 562-566 ◽  
Author(s):  
Chen Hu Yuan ◽  
Mu Zhang ◽  
Sheng Wei Gao ◽  
Wei Wang ◽  
Xing Tao Sun
Keyword(s):  
Neural Network ◽  
Power Transformer ◽  
Fault Analysis ◽  
Diagnostic Methods ◽  
Point Temperature ◽  
State Evaluation ◽  
Study Results ◽  
Design Production ◽  
Type Power ◽  
Self Organizing

Dry-type power transformer was used widely because of its advantages. But unplanned outage effect to construct a strong intelligent power grid because of various stress. Dry-type power transformer’s fault repair time is long and impossible to repair. So it is very important to realize state maintains of dry-type transformer through state monitor and diagnosis. Based on current diagnostic methods, this paper proposed using self-organizing neural network to realize dry-type power transformer the key point temperature parameters of grading evaluation and then to realize the real-time state evaluation and analysis of failure causes. Study results to prolong the dry-type power transformer life and its design production provide theoretical guidance, in order to reduce and avoid dry-type power transformer failure.

scholarly journals DGA Method Implementation for Incipient Fault Analysis using Gas Concentrations

2021 ◽  
pp. 1-9
Author(s):  
Jyoti Singh ◽  
Dr. Prateek Nigam ◽  
Achie Malviya
Keyword(s):  
Neural Network ◽  
Error Detection ◽  
Power Transformer ◽  
Fault Analysis ◽  
Transformer Oil ◽  
Ratio Method ◽  
Detection Accuracy ◽  
Data Sets ◽  
Matlab Software ◽  
Reliable Performance

Power transformers are essential devices for the durable and reliable performance of an electrical system. the main objective of this study is to analyze three classical diagnosis techniques to identify incipient faults in Transformer oil using Rogers’s Ratio Method, Doernenburg Ratio Method, and ANN which is a type of artificial intelligence learning method. Implementation of the system in MATLAB software for each diagnosis method and compare their accuracy and efficiency and hence design three diagnosis methods of DGA for condition assessment of Power Transformer. And the analysis on the MATLAB software shall be carried so as to detect the best method for detection of a certain type of fault and the best suited method for overall fault analysis for a certain data sets out of the three methods. This technique utilizes the learning capacity of that artificial neural network has been shown to be more efficient in detecting different mistakes. The overall error detection accuracy of such gas neural network study was found to be 73.8 percent.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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