Fault Classification in Transmission Lines Using Wavelet and CNN

Paul, Debdyuti; Mohanty, Subodh Kumar

doi:10.1109/i2ct45611.2019.9033687

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…Researchers have explored different aspects of fault classification, including fault inception angles, resistance faults, fault locations, and fault types, leading to a lack of uniformity. In addition, no particular variable has proven to be adequate to be considered the best variable for fault categorization, despite the high accuracy in some of them, and the accuracy varied between studies [28][29][30][31]. Part of the challenge is considering most of the parameters that can affect the fault classification in Table 2 that were not presented in [28,34], or considering some of them, as in [18,25,27,30,31,38], while the others they did not even mention, as in [22,26].…”

Section: Comparison To Previous Methodsmentioning

confidence: 99%

“…Although they work hard to reach high accuracy for categorization of the non-stationary signals, they disregard the crucial matter of the data type. However, different articles have stated that high accuracy can be reached by using both of the three-phase signals (voltage and current) as input to machine learning [21,22,[24][25][26][27][28]. While others used only one variable of those previously mentioned [18,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Altaie,

Abderrahim,

Alkhazraji

2024

Sensors

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This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

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Section: Comparison To Previous Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Altaie,

Abderrahim,

Alkhazraji

2024

Sensors

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show abstract

“…Both versions of the VGGNet include two completely connected layers, each with a total of 4096 channels. These layers are then followed by a further fully connected layer that consists of 1000 channels and is used to categorise 1000 labels [13]- [15]. In the very last completely connected layer, the softmax layer is the one that's employed for classification.…”

Section: Vggnet and Its Architecturementioning

confidence: 99%

Refurbished and improvised model using convolution network for autism disorder detection in facial images

Kaur

Gupta²

2023

IJEECS

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<span lang="EN-US">The main quality of deep learning over conventional machine learning (ML) techniques empowers firsthand uses in processing of images, speech recognition, medical imaging, machine translation and robotics, computer vision, and numerous other fields. The purpose of this study is to assess algorithms of deep Learning for person with the disorder of autism. This disorder is developing disorder that causes significant communicative, social and behavioral difficulties in those who have it. In this research paper, the Enhanced version of convolution network is discussed. Visual geometry group (VGG), is one of model of the convolution neural network which has essential features of convolution neural network (CNN). The VGG 16 net is employed to calculate the processes that can be used to classify this disorder with improved accuracy. The preprocessing of the image data is done. The VGG 16 convolution network is used to classify between autism spectrum <span>disorder (ASD) and Non-ASD. Finally, the algorithm's efficacy is considered based on its accuracy performance. The VGG 16 net algorithm produces better results with an accuracy of 68.54%, compared with the normal CNN algorithm.</span></span>

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“…For example, in [12], raw signals were transformed into two-dimensional grayscale images based on wavelet transform and deep CNN to extract robust features. In [13], a novel fault detection and classification method was proposed by using the DWT and CWT filter banks.…”

Section: Traditional Fault Detection and Intelligent Fault Detectionmentioning

confidence: 99%

An effective zero-shot learning approach for intelligent fault detection using 1D CNN

2022

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Data-driven fault detection techniques have attracted extensive attention in engineering, industry and many other areas in recent years. In many real applications, the following situation often occurs: data for certain types of faults (unseen faults) are not available to train models that are used for fault detection. Such a scenario can occur when data collection becomes highly time-consuming or destructive. To address this challenging problem, a novel fault detection method using zero-shot learning (ZSL) is proposed in this paper, which contains three phases: feature extraction, label embedding, and feature embedding. The method first extracts features from raw signals by applying a one-dimensional convolutional neural network (1D CNN), then builds semantic descriptions (human-defined) as fault attributes shared between seen faults and unseen faults, and finally uses a bi-linear compatibility function to find the highest-ranking fault type. The proposed semantic space based zero-shot learning with 1D CNN is called SSB-ZSL-1DCNN. The cosine distance is used to measure the similarity between feature embeddings and fault attributes. An important characteristic of SSB-ZSL-1DCNN is that the model, trained using only samples of seen faults, can be used to detect unseen defects. To evaluate the proposed method, two case studies are designed based on two well-known benchmarks (the Tennessee-Eastman chemical control process and the rolling bearing experiments at the Case Western Reserve University, respectively). The results demonstrate that the proposed method shows remarkable performance in detecting unseen faults.

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Fault Classification in Transmission Lines Using Wavelet and CNN

Cited by 10 publications

References 18 publications

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Refurbished and improvised model using convolution network for autism disorder detection in facial images

An effective zero-shot learning approach for intelligent fault detection using 1D CNN

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