A machine learning-based approach for comprehensive fault diagnosis in transmission lines

França, Isternândia Araújo; Vieira, Cynthia Wanick; Ramos, Daniel Correa; Sathler, Lara Hoffmann; Carrano, Eduardo G.

doi:10.1016/j.compeleceng.2022.108107

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Considering that they usually cover long distances, in the event of a failure, they may have an impact on a wide area. Therefore, the fault diagnosis method [2,3] for overhead transmission lines based on fault recordings is particularly important. By analyzing the detailed waveform information when the fault occurs, such as current and voltage waveform data [4] (as shown in Figure 1), these methods are able to accurately locate and identify line faults, which helps to quickly troubleshoot the faults and restore the power supply, and thus ensures the safe operation of the power grid and stable power supply.…”

Section: Introductionmentioning

confidence: 99%

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Jia,

Huang,

Han

et al. 2024

IET Communications

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Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Jia,

Huang,

Han

et al. 2024

IET Communications

View full text Add to dashboard Cite

show abstract

“…Therefore, to maintain damaged components and reduce downtime, it is of significant importance to accurately diagnose transmission line faults and rapidly eliminate them. Against this background, efficient fault diagnosis schemes for transmission lines are urgently needed to remove these faults and guarantee the safe operation of power systems [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Attention Temporal Convolutional Network for Transmission Line Fault Diagnosis via Comprehensive Feature Extraction

Gao,

et al. 2023

Energies

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Traditional transmission line fault diagnosis approaches ignore local structure feature information during feature extraction and cannot concentrate more attention on fault samples, which are difficult to diagnose. To figure out these issues, an enhanced feature extraction-based attention temporal convolutional network (EATCN) is developed to diagnose transmission line faults. The proposed EATCN suggests a new comprehensive feature-preserving (CFP) technique to maintain the global and local structure features of original process data during dimension reduction, where the local structure-preserving technique is incorporated into the principal component analysis model. Furthermore, to diagnose transmission line faults more effectively, a CFP-based attention TCN scheme is constructed to classify the global and local structure features of a fault snapshot dataset. To be specific, to cope with the gradient disappearance problem and improve learning capability, a skip connection attention (SCA) network is developed by incorporating a skip connection structure and two fully connected layers into the existing attention mechanism. By combining the developed SCA network with the conventional TCN’s residual blocks, an EATCN-based diagnosis model is then constructed to dynamically pay attention to various imported global and local structure features. Detailed experiments on the datasets of the simulated power system are performed to test the effectiveness of the developed EATCN-based fault diagnosis scheme.

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“…However, the transmission line's fault always affects the power supply and reliability of the power system [2]. Under this background, efficient fault diagnosis schemes for the transmission line are urgently needed to remove these faults and guarantee the power system is running safely [3]. For many data-driven transmission line recognition models, the original datasets are often used as the input.…”

Section: Introduction 1motivation and Incitementmentioning

confidence: 99%

A Novel Convolutional LSTM Network Based on the Enhanced Feature Extraction for the Transmission Line Fault Diagnosis

Lu,

Zheng,

Gao

et al. 2023

Processes

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Recently, the traditional transmission line fault diagnosis approaches cannot handle the variables’ dynamic coupling properties, and they also ignore the local structure feature information during the feature extraction. To figure out these issues, a novel enhanced feature extraction based convolutional LSTM (ECLSTM) approach is developed to diagnose the transmission line fault in this paper. Our work has three main contributions: (1) To tackle the dynamic coupling characteristics of the process variables, the statistics analysis (SA) method is first employed to calculate different statistical features of the transmission line’s original data, where the original datasets are transformed into the subsequently used statistics datasets; (2) The statistics comprehensive feature preserving (SCFP) is then proposed to maintain both the global and local structure features of the constructed statistics datasets, where the locality structure preserving technique is incorporated into the principal component analysis (PCA) model to extract the features from the statistics datasets; (3) To effectively diagnose the transmission line’s fault, the SCFP based convolutional LSTM fault diagnosis scheme is constructed to classify the global and local statistical structure features of fault snapshot dataset, because of its ability to exploit the temporal dependencies and spatial correlations of the extracted statistical features. Detailed experiments and comparisons on the datasets of the simulated power system are performed to prove the excellent performance of the ECLSTM based fault diagnosis scheme.

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A machine learning-based approach for comprehensive fault diagnosis in transmission lines

Cited by 5 publications

References 22 publications

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

A Novel Attention Temporal Convolutional Network for Transmission Line Fault Diagnosis via Comprehensive Feature Extraction

A Novel Convolutional LSTM Network Based on the Enhanced Feature Extraction for the Transmission Line Fault Diagnosis

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