Artificial neural network based identification of deviation in frequency response of power transformer windings

Gandhi, Ketan R.; Badgujar, Ketan P.

doi:10.1109/aicera.2014.6908217

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…In this work, only two groups of setups are used. Gandhi et al (2014) [15] used 9 indices and 90 cases for three-layer ANN. The major drawback of this work is that all faults are applied to a single transformer model.…”

Section: Application Of Machine Learning In Framentioning

confidence: 99%

“…Many recent studies have been focused on FRA interpretation to detect the extent and the type of mechanical fault. Different algorithms have been proposed for this purpose that can be categorized into three main groups: simulation models (circuit models/FEM models) [8][9][10], numerical indices [11][12][13], and artificial intelligence (AI) techniques [14,15]. However, these methods have some drawbacks and limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) and machine learning (ML) methods have been of great interest lately for diagnosing transformer faults using FRA results and are gaining popularity due to their effectiveness and high accuracy. Using AI methods, it is possible to classify the changes in the FRA traces and connect them to different fault types and even fault locations [14,15]. These methods are based on techniques such as genetic algorithms, fuzzy logic, support vector machines, and neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The major dilemma of using AI methods is the need for a database from real power transformers, and such a database usually is not available. Some studies propose to use the outputs of model experiments and circuit models as a database [14,15]. However, the applicability of these models for large-scale power transformers is itself an issue.…”

Section: Introductionmentioning

confidence: 99%

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

Tahir

Tenbohlen

2021

Energies

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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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Section: Application Of Machine Learning In Framentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tahir

Tenbohlen

2021

Energies

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“…References [19] and [20] reported that they used advanced signal-processing techniques (ASPT) and stator-windings-currents for stator winding fault detection of the IM. The authors in [21] analysed the deviation in frequency response (FR) by using artificial-neural-network (ANN) for faults detection. The electrical fault for induction motors field-windings was detection for first approach through FRA technique with static excitation was reported in [22].…”

Section: Literature or Bibliography Surveymentioning

confidence: 99%

Phase to phase fault detection of 3-phase induction motor using FRA technique

Alawady

Yousof

Azis

et al. 2020

IJPEDS

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The purpose for preparing this paper is to innovate a new method to detection and diagnosis the three-phase induction motor electrical failures, especially the failures that occur in Stator winding. Stator winding breakdown largely contributes to induction motor failures. To understand internal fault in induction motors winding, four cases studies of different three-phase induction motors (TPIM) were analysed according to two conditions: normal winding condition and windings shorted between two phases. In this paper, the measurement of frequency response analysis (FRA) on the stator winding with inter-phase short is presented. Additionally, Frequency Response Analysis (FRA) interpretation technique classify and quantify the fault is also proposed. For interpretation of the FRA, a statistical indicator, which is NCEPRI algorithm is used for comparison the measured responses.

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FRA interpretation using numerical indices: State-of-the-art

Samimi

Tenbohlen

2017

International Journal of Electrical Power & Energy Systems

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Artificial neural network based identification of deviation in frequency response of power transformer windings

Cited by 12 publications

References 10 publications

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

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

Phase to phase fault detection of 3-phase induction motor using FRA technique

FRA interpretation using numerical indices: State-of-the-art

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