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

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.

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Power transformer equivalent circuit identification by artificial neural network using frequency response analysis

2006 IEEE Power Engineering Society General Meeting ◽

10.1109/pes.2006.1708931 ◽

2006 ◽

Cited By ~ 10

Author(s):

G.M.V. Zambrano ◽

A.C. Ferreira ◽

L.P. Caloba

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Frequency Response ◽

Equivalent Circuit ◽

Power Transformer ◽

Response Analysis ◽

Frequency Response Analysis ◽

Artificial Neural

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

Energies ◽

10.3390/en14144242 ◽

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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Detection of Transformer Defects in Smart Environment Using Frequency Response Analysis and Artificial Neural Network Based on Data-Driven Systems

10.1109/sgc54087.2021.9664092 ◽

2021 ◽

Author(s):

Reza Behkam ◽

Mahdi Salay Naderi ◽

G.B. Gharehpetian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Frequency Response ◽

Data Driven ◽

Response Analysis ◽

Smart Environment ◽

Frequency Response Analysis ◽

Driven Systems ◽

Artificial Neural

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An Efficient Techniques used for Diagnosis of Mitral Valve Regurgitation Severity

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4299.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 486-491

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Image Segmentation ◽

Mitral Valve ◽

Mitral Valve Regurgitation ◽

Classification Model ◽

Mitral Valve Stenosis ◽

Valve Regurgitation ◽

Artificial Neural ◽

Artificial Neural Network Ann

Mitral valve diseases are more common nowadays and might not show up any symptoms. The earlier diagnosis of mitral valve abnormalities such as mitral valve stenosis, mitral valve prolapses and mitral valve regurgitation is most important in order to avoid complex situation. Many existing methodologies such as heart sound investigation model, 3-layered artificial neural network (ANN) of phonocardiogram recordings, 3-layer artificial neural network (ANN) of phonocardiogram recording, echocardiography techniques and so on are there for Mitral Valve diagnosis, but still most of the methods suffer from inefficient image segmentation and misclassification problems. In order to address this issue, this paper proposes two techniques namely 1) Deep Learning based Convolutional Neural Network (CNN) model for Mitral Valve classification model meant for diagnosis and edge detection-based segmentation model to enhance the classifier accuracy. 2) Watershed Segmentation for Mitral Valve identification and image segmentation and Xception model with Random Forest (RF) classifier for training and classification. The proposed models are evaluated in terms of three parameters namely accuracy, sensitivity and specificity, which proved that the proposed models are efficient and appropriate for Mitral Valve diagnosis.

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A new method for evaluation of transformer drying process using transfer function analysis and artificial neural network

Archives of Electrical Engineering ◽

10.2478/aee-2013-0011 ◽

2013 ◽

Vol 62 (1) ◽

pp. 153-162

Author(s):

Hormatollah Firoozi ◽

Mehdi Bigdeli

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Functions ◽

Power Transformer ◽

Function Analysis ◽

Active Part ◽

Drying Process ◽

Transfer Function Analysis ◽

Artificial Neural ◽

Artificial Neural Network Ann

Abstract Since a few years ago, there is an increasing interest for utilization of transfer functions (TF) as a reliable method for diagnosing of mechanical faults in transformer structure. However, this paper aims to develop the application of TF method in order to evaluate the drying quality of active part during the manufacturing process of transformer. To reach this goal, the required measurements are carried out on 50 MVA 132 KV/33 KV power transformer when active part is placed in the drying chamber. Two different features extracted from the measured TFs are then used as the inputs to artificial neural network (ANN) to give an estimate for required time in drying process. Results show that this new represented method could well forecast the required time. The results obtained from this method are valid for all the transformers which have the same design.

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