Detection of probability of occurrence, type and severity of faults in transformer using frequency response analysis based numerical indices

Bigdeli, Mehdi; Azizian, Davood; Gharehpetian, Gevork B.

doi:10.1016/j.measurement.2020.108322

Cited by 32 publications

(11 citation statements)

References 27 publications

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“…One of the best ways to compare TFs with reference TF is to use numerical and statistical indices. Although numerical indices have been used in the diagnosis of transformer defects [9]- [12], [16]- [18], however, a comprehensive comparison of the performance of these indices for the purpose of training the intelligent classifiers is not seen in the literature. Therefore, in this paper, it is proposed that each of the indices be used separately as a feature.…”

Section: The Proposed Methods For Extracting the Featuresmentioning

confidence: 99%

“…Various methods have been proposed in the literature to identify the type of fault and classify them, which can be divided into two main categories. The first category includes methods according to which faults' classification is solely based on the rate of variations in numerical indices (statistical and mathematical indices) in specific frequency ranges [9]- [18]. The second category includes methods that use intelligent classifiers to distinguish faults [19]- [25].…”

Section: Introductionmentioning

confidence: 99%

“…However, three faults AD, RD, and SC are clustered, and DSV and DCS are not considered in the fault detection process. In [18], a new index called IMGM is proposed for fault type detection and all five defects have been considered. The IMGM distinguishes faults with probability and it is unable to certainly detect the type of fault.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

2021

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With the expansion of the use of frequency response analysis (FRA) as a reliable tool for fault detection in transformers, more capabilities of this method are discovered every day. So that today the number of transformer faults that can be identified by FRA method has also increased. One of the most critical steps in fault detection with FRA is to distinguish faults and classify them in different classes. In this paper, well-known intelligent classifiers (probabilistic neural network, decision tree, support vector machine, and k-nearest neighbors) are used to classify transformer faults. For this purpose, the necessary measurements are performed on the model transformers under the healthy condition and under different fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency ranges of the measured transfer functions of the transformer, a new feature based on numerical and statistical indices for training and validation of classifiers is proposed. After completing the training process, the performance of the classifiers is evaluated and compared by applying the data obtained from real transformers. INDEX TERMS Transformer, fault type detection, frequency response analysis (FRA), intelligent classifiers, measurement, numerical indices.

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Section: The Proposed Methods For Extracting the Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

2021

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“…Different approaches for the interpretation of FRA measurements are reported in recent literature on the application of intelligent classifiers. For instance, the combination of numerical indices and intelligent classifiers is explored in [16,[23][24][25][26]. References [16,23] use numerical indices as input to neural networks and discuss the use of support vector machine (SVM) for fault type recognition in power transformers.…”

Section: Frequency Response Analysismentioning

confidence: 99%

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

et al. 2021

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Frequency response analysis is a powerful tool for mechanical fault diagnostics in power transformers. However, interpretation schemes still today depend on expert analyses, mainly because of the complex structure of power transformers. One of the fundamental shortcomings of experimental investigations is that mechanical deformations cannot be managed on real transformers to obtain data for different scenarios because they are too destructive. To address this issue in a systematic way, the current research used a specially designed laboratory transformer model that allows mechanical defects to be introduced so its frequency response can be evaluated under different conditions. The key feature of this model is the non-destructive interchangeability of its winding sections, allowing reproducibility and repeatability of frequency response measurements. Numerical indices were compared over key performance indicators (linearity, sensitivity and monotonicity). The analysis indicated that comparative standard deviation offered promising results for evaluation of mechanical deformations on the laboratory winding model given its monotonic behaviour, sensitivity and linear increase with fault severity. Additionally, support vector machine learning, radial basis function neural network and the statistical k-nearest neighbour method were used for fault classification with different strategies and configurations. While limited data from different transformers are used in the available literature, the approach discussed here considers 371 measurements from the same transformer model. The test results are supportive and demonstrate great accuracy when machine learning is used for winding fault classification.INDEX TERMS Condition monitoring, frequency response analysis interpretation, machine learning, numerical indices, power transformers, radial basis function, support vector machines.

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“…In recent studies of FRA interpretation, there has been an increase in the use of machine learning algorithms to help in developing objective interpretations and to reduce dependency on expert analyses [5][6][7]. The main challenge now is building a sufficient database to train and test these algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

et al. 2021

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Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

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Detection of probability of occurrence, type and severity of faults in transformer using frequency response analysis based numerical indices

Cited by 32 publications

References 27 publications

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

Intelligent Classifiers in Distinguishing Transformer Faults Using Frequency Response Analysis

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

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