Equivalent gradient area based fault interpretation for transformer winding using binary morphology

Lin, Huan; Zhang, Ziwei; Wenhu, Tang; Yan, Joseph

doi:10.1109/tdei.2017.006237

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…[k × H], which is the height of the SE, is adaptable to each FR diagram with different degrees of deviation. According to the result proved in [36], k and W are set to '0.01′ and '2,' respectively. Figure 2b shows an example of an eroded image, where the SE defined in Equation (2) is used to erode the binary image shown in Figure 2a.…”

Section: Application Of Erosionmentioning

confidence: 99%

“…An improved approach, according to binary erosion and first anti-resonance, is adopted in this paper to divide the frequency sub-band dynamically. A similar method was proposed and validated in the authors' previous research [36].…”

Section: Introductionmentioning

confidence: 99%

“…( ) To summarize, the algorithms illustrated in Figure 4 provide a method that divides the frequency range into five sub-bands specifically for each case of FR. This method is based on the authors' previous work in [36], with a slight improvement. The performance of this approach was proven in [36], so it will not be repeated in this article.…”

mentioning

confidence: 99%

“…This method is based on the authors' previous work in [36], with a slight improvement. The performance of this approach was proven in [36], so it will not be repeated in this article.…”

mentioning

confidence: 99%

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Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

et al. 2018

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Frequency response analysis (FRA) demonstrates significant advantages in the diagnosis of transformer winding faults. The instrument market desires intelligent diagnostic functions to ensure that the FRA technique is more practically useful. In this paper, a hierarchical dimension reduction (HDR) classifier is proposed to identify types of typical incipient winding faults. The classifier procedure is hierarchical. First, measured frequency response (FR) curves are preprocessed using binarization and binary erosion to normalize FR data. Second, the pre-processed data are divided into groups according to the definition of dynamic frequency sub-bands. Then, hybrid algorithms comprised of two conventional and two novel quantitative indices are used to reduce the dimension of the FR data and extract the features for identifying typical types of transformer winding faults. The classifier provides an integration of a priori expertise and quantitative analysis in the furtherance of the automatic identification of FR data. Twenty-six sets of FR data from different types of power transformers with multiple types of winding faults were collected from an experimental simulation, literature, and real tests performed by a grid company. Finally, real case studies were conducted to verify the performance of the HDR classifier in the automatic identification of transformer winding faults.

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Section: Application Of Erosionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…This method is based on the authors' previous work in [36], with a slight improvement. The performance of this approach was proven in [36], so it will not be repeated in this article.…”

mentioning

confidence: 99%

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Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

et al. 2018

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“…The studies are belonging to the traditional + localization methods, however, which require specific rules or inferences for different transformers [51]. Fault localization via graphical method could standardize and visualize diagnostic process, and reduce the interferences [52], which aroused attentions in recent two years. The concept ‘fault identification’ includes both fault type classification and fault localization.…”

Section: Introductionmentioning

confidence: 99%

Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model

Duan

et al. 2019

Sensors

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The idea of Ubiquitous Power Internet of Things (UPIoTs) accelerates the development of intelligent monitoring and diagnostic technologies. In this paper, a diagnostic method suitable for power equipment in an interference environment was proposed based on the deep Convolutional Neural Network (CNN): MobileNet-V2 and Digital Image Processing (DIP) methods to conduct fault identification process: including fault type classification and fault localization. A data visualization theory was put forward in this paper, which was applied in frequency response (FR) curves of transformer to obtain dataset. After the image augmentation process, the dataset was input into the deep CNN: MobileNet-V2 for training procedures. Then a spatial-probabilistic mapping relationship was established based on traditional Frequency Response Analysis (FRA) fault diagnostic method. Each image in the dataset was compared with the fingerprint values to get traditional diagnosing results. Next, the anti-interference abilities of the proposed CNN-DIP method were compared with that of the traditional one while the magnitude of the interference gradually increased. Finally, the fault tolerance of the proposed method was verified by further analyzing the deviations between the wrong diagnosing results with the corresponding actual labels. Experimental results showed that the proposed deep visual identification (CNN-DIP) method has a higher diagnosing accuracy, a stronger anti-interference ability and a better fault tolerance.

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Classification of power quality disturbances using visual attention mechanism and feed-forward neural network

Zhang

Zhou

2022

Measurement

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Equivalent gradient area based fault interpretation for transformer winding using binary morphology

Cited by 7 publications

References 12 publications

Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model

Classification of power quality disturbances using visual attention mechanism and feed-forward neural network

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