Optimized Artificial Neural Network for the detection of incipient faults in power transformer

2014 ◽  
Author(s):  
Fathiah Zakaria ◽  
Dalina Johari ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Transformer ◽  
Incipient Faults ◽  
Artificial Neural

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.

scholarly journals Assessment of the current technical condition of electrical equipment based on an artificial neural network

2021 ◽  
Vol 2131 (5) ◽  
pp. 052049
Author(s):  
V Z Manusov ◽  
M R Otuzbaev ◽  
M A Scherbinina ◽  
G V Ivanov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Efficiency ◽  
Power Transformer ◽  
Reliability And Validity ◽  
Electrical Equipment ◽  
Technical Condition ◽  
Computer Assisted ◽  
Technical Diagnostics ◽  
Artificial Neural

Abstract Assessment of the current technical condition is an important task, so the state of electrical equipment depends on its further operability. Thanks to modern computing devices, it is possible to implement actively artificial intelligence and computer-assisted learning methods that allow achieving high efficiency in data processing. A study was conducted and an algorithm for diagnosing the technical condition based on an artificial neural network was developed. A model based on a multilayer perceptron is proposed, which allows evaluating the technical condition of a high-voltage power transformer. The result of the technical diagnostics of the model is the assignment of the condition to one of the five classes, proposed by the guidelines presented by the International Council on Large Electrical Systems. The methodology is presented on the example of a 250 MVA transformer with a certain defect history, which allowed us to show the reliability and validity of the obtained results. It is shown that the use of the proposed model makes it possible to achieve accuracy in determining the technical condition of 0.95. The introduction of this model into an automated monitoring and diagnostics system will allow assessing the technical condition of electrical equipment in real time with sufficient accuracy.

scholarly journals Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3227
Author(s):  
Mehran Tahir ◽  
Stefan Tenbohlen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Frequency Response ◽  
Power Transformer ◽  
Classification Model ◽  
Response Analysis ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Electrical Faults ◽  
Artificial Neural Network Ann

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

Internal fault diagnosis of power transformer using artificial neural network

2021 ◽  
Author(s):  
G.S. Naganathan ◽  
M. Senthilkumar ◽  
S. Aiswariya ◽  
S. Muthulakshmi ◽  
G. Santhiya Riyasen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Internal Fault ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document