Transmission Line Fault Detection Research Articles

Detection and Classification of EHV Transmission Line Faults Based on Sign of Reactive Power

Detection and Classification of EHV Transmission Line Faults Based on Sign of Reactive Power

Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines

ABSTRACT Fault detection, classification, and location prediction are crucial for maintaining the stability and reliability of modern power systems, reducing economic losses, and enhancing system protection sensitivity. This paper presents a novel Hierarchical Deep Learning Approach (HDLA) for accurate and efficient fault diagnosis in transmission lines. HDLA leverages two-stage transformer-based classification and regression models to perform Fault Detection (FD), Fault Type Classification (FTC), and Fault Location Prediction (FLP) directly from synchronized raw three-phase current and voltage samples. By bypassing the need for feature extraction, HDLA significantly reduces computational complexity while achieving superior performance compared to existing deep learning methods. The efficacy of HDLA is validated on a comprehensive dataset encompassing various fault scenarios with diverse types, locations, resistances, inception angles, and noise levels. The results demonstrate significant improvements in accuracy, recall, precision, and F1-score metrics for classification, and Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for prediction, showcasing the effectiveness of HDLA for real-time fault diagnosis in power systems.

Advancements In Machine Learning Algorithms for Enhanced Fault Analysis and Categorization in Power Systems

With the growing number of sources, complexity, and dynamic elements of power systems that are prone to disturbance and electrical faults, conventional methods of fault detection are lagging in power system protection. Major faults are exposed to transmission lines due to several factors, such as climate change, heat, lightning strokes, sudden spikes of current and voltage etc. Protecting these lines from faults is cumbersome using traditional methods. However, with the increasing complexity of transmission lines in power systems, advanced technologies such as machine learning come into play for prior fault detection and identification. ML is becoming a popular and intelligent technique for monitoring and predicting health and diagnosing faults in transmission lines of power systems. Since ML is evolving at a faster pace and given the need for and growth of advanced technologies like ML and artificial intelligence, this paper emphasizes comparative analysis of various ML algorithms for transmission line fault analysis. Comparisons for various line faults (LL, LLG, LG, LLL, LLLG, no fault) in power systems using machine learning techniques have been done in terms of accuracy, precision, and other performance measures. The results of the accuracy of various algorithms are represented as tabulated facts in this paper. The Python programming language has been utilized over a dataset to calculate results. For each algorithm, the confusion matrix shows the accuracy of predicting faults over the testing data after training the model or algorithm on the training data. Upon completion of analysis and comparison of MLT's like LR, SVM, DT, RF, K-NN, and gradient descent for analysis of transmission line fault detection, RF and DT machine learning algorithms provided the best accuracy and results. Finally, this paper gives a brief overview of various line faults, ML algorithms accuracy for each transmission fault in a confusion matrix and tabular format and concluded the best machine learning algorithms for power system transmission line fault identification.

Fault Type and Fault Location Detection in Transmission Lines with 6-Convolutional Layered CNN

In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.

Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

AbstractFaults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

Machine learning advances in transmission line fault detection: A literature review

Machine learning advances in transmission line fault detection: A literature review

Development of overcurrent relay based on wavelet transform for fault detection in transmission line

This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.

FSLEdge: An energy-aware edge intelligence framework based on Federated Split Learning for Industrial Internet of Things

Federated Learning (FL) enabled edge computing has been widely used in training complex deep learning models by coordinating various heterogeneous resources in Industrial Internet of Things (IIoT). However, the existing FL solutions may cause long training time or huge energy consumption for resource-constrained terminal or edge nodes. To address this issue, we propose an adaptive edge intelligence offloading framework based on Federated Split Learning (FSL) and Reinforcement Learning (RL), named FSLEdge, for IIoT systems. To begin with, a Split Learning (SL) approach was added to FL to reduce the training time and energy consumption by allowing local devices to offload some training layers into the edge servers while ensuring the protection of private data. Additionally, FSLEdge adopts RL to find the optimal split point to adaptively identify which is the best layers of the deep learning models should be offloaded into the edge servers with the goal of reducing the training time and energy consumption. Experimental studies are conducted under two different datasets: one public, CIFAR10, and one private, GridDataset, a transmission line fault detection dataset in smart grid domain. And the results show that the training time and energy consumption of the system by our proposed FSLEdge can be reduced by 10% and 21%, respectively, compared to the classical FL, without sacrificing accuracy.

Fault Detection and Localization in Transmission Lines Using Adaptive Neural Fuzzy Inference System

Fault Detection and Localization in Transmission Lines Using Adaptive Neural Fuzzy Inference System

An algorithm for power transmission line fault detection based on improved YOLOv4 model

In response to the escalating demand for real-time and accurate fault detection in power transmission lines, this paper undertook an optimization of the existing YOLOv4 network. This involved the substitution of the main feature extraction network within the original YOLOv4 model with a lighter EfficientNet network. Additionally, the inclusion of Grouped Convolution modules in the feature pyramid structure replaced conventional convolution operations. The resulting model not only reduced model parameters but also effectively ensured detection accuracy. Moreover, in enhancing the model's reliability, data augmentation techniques were employed to bolster the robustness of the power transmission line fault detection algorithm. This optimization further utilized the DIoU loss function to stabilize target box regression. Comparative experiments demonstrated the improved YOLOv4 model's superior performance in terms of loss function optimization while significantly enhancing detection speed under equivalent configurations. The parameter capacity was reduced by 81%, totaling merely 43.65 million, while the frame rate surged by 85% to achieve 24 frames per second. These experimental findings validate the effectiveness of the algorithm.

Transmission line fault detection based on anchor frame a priori and attention mechanism

YOLO target detection algorithm is the mainstream method for image-based defect detection of transmission line insulators. However, the existing model complexity is large. A reasonable and effective parameter compression method is urgently needed as a prerequisite to lay the foundation for solving the dilemma of UAV edge equipment deployment. At the same time, the insulator defect images taken by UAVs have complex backgrounds and small defect sizes, which are prone to misdetection and omission. It is needed to improve the channel domain of the CBAM (convolution block attention module) attention mechanism and solve its problem of missing channel information due to dimensionality reduction. The improved CBAM is added to the backbone network of YOLOv5s. The goal is to enable the model to identify and pinpoint the critical objectives with greater precision. The model is combined with Cross Entropy Loss and Lovasz-Softmax Loss weighting, which makes the network converge more stably during the training process, and the accuracy rate is also somewhat improved.

Lightweight Transmission Line Fault Detection Method Based on Leaner YOLOv7-Tiny.

Aiming to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones, this study proposes a novel lightweight model called Leaner YOLOv7-Tiny. The primary goal is to swiftly and accurately detect typical faults in transmission lines from aerial images. This algorithm inherits the ELAN structure from YOLOv7-Tiny network and replaces its backbone with depthwise separable convolutions to reduce model parameters. By integrating the SP attention mechanism, it fuses multi-scale information, capturing features across various scales to enhance small target recognition. Finally, an improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results demonstrate a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection.

Improved Relay Algorithm for Detection and Classification of Transmission Line Faults in Monopolar HVDC Transmission System Using Signum Function of Transient Energy

Improved Relay Algorithm for Detection and Classification of Transmission Line Faults in Monopolar HVDC Transmission System Using Signum Function of Transient Energy

Transmission Line Fault Detection and Classification Based on Improved YOLOv8s

Transmission lines are an important component of the power grid, while complex natural conditions can cause fault and delayed maintenance, which makes it quite important to locate and collect the fault parts efficiently. The current unmanned aerial vehicle (UAV) inspection on transmission lines makes up for these problems to some extent. However, the complex background information contained in the images collected by power inspection and the existing deep learning methods are mostly highly sensitive to complex backgrounds, making the detection of multi-scale targets more difficult. Therefore, this article proposes an improved transmission line fault detection method based on YOLOv8s. The model not only detects defects in the insulators of power transmission lines but also adds the identification of birds’ nests, which makes the power inspection more comprehensive in detecting faults. This article uses Triplet Attention (TA) and an improved Bidirectional Feature Pyramid Network (BiFPN) to enhance the ability to extract discriminative features, enabling higher semantic information to be obtained after cross-layer fusion. Then, we introduce Wise-IoU (WIoU), a monotonic focus mechanism for cross-entropy, which enables the model to focus on difficult examples and improve the bounding box loss and classification loss. After deploying the improved method in the Win10 operating system and detecting insulator flashover, insulator broken, and nest faults, this article achieves a Precision of 92.1%, a Recall of 88.4%, and an mAP of 92.4%. Finally, we conclude that in complex background images, this method can not only detect insulator defects but also identify power tower birds’ nests.

Convolutional Neural Network for Transmission Line Fault Diagnosis Based on Signal Segmentation Approach

Convolutional Neural Network (CNN) is a Deep Learning algorithm that can take in an input signal, assign importance (learnable weights and biases) to various aspects of the signals and distinguish between them. The CNN algorithm trains a sample and obtains a CNN model capable of identifying different fault types to analyse transmission line faults. Fault analysis methods typically require feature extraction to synthesise the relevant and non-redundant information from the raw signals. However, these traditional methods are time-consuming and inconsistent, and they can produce biased results due to the reliance on human expertise and experience. Hence, this research focused on developing an intelligent system for fault detection in a three-phase transmission line using CNN. This research aims to develop a CNN model for automatic fault detection in a three-phase transmission line and evaluate the performance of the CNN model for analysing transmission line faults. The three-phase transmission line model was developed using MATLAB-Simulink. CNN was implemented to detect transmission line faults. The performance of CNN based on the signal segmentation approach was evaluated through three different types of data: ideal and noise-added signal data. The simulation result shows good performance accuracy of 99.11% for the ideal case, 99.36%, and 99.39% for 20 dB and 30 dB noise-added cases, respectively. The result shows that a higher noise value in transmission line fault current could increase the performance of CNN. In conclusion, the utilisation of CNN based on a signal segmentation approach for transmission line fault analysis has showed promising performance.

Fault detection through discrete wavelet transform in overhead power transmission lines

AbstractTransmission lines are a very important and vulnerable part of the power system. Power supply to the consumers depends on the fault‐free status of transmission lines. If the normal working condition of the power system is disturbed due to faults, the persisting fault of long duration results in financial and economic losses. The fault analysis has an important association with the selection of protective devices and reliability assessment of high‐voltage transmission lines. It is imperative to devise a suitable feature extraction tool for accurate fault detection and classification in transmission lines. Several feature extraction techniques have been used in the past but due to their limitations, that is, for use in stationary signals, limited space in localizing nonstationary signals, and less robustness in case of variations in normal operation conditions. Not suitable for real‐time applications and large calculation time and memory requirements. This research presents a discrete wavelet transform (DWT)‐based novel fault detection technique at different parameters, that is, fault inception and fault resistance with proper selection of mother wavelet. In this study, the feasibility of DWT using MATLAB software has been investigated. It has been concluded from the simulated data that wavelet transform together with an effective classification algorithm can be implemented as an effective tool for real‐time monitoring and accurate fault detection and classification in the transmission lines.

A comparative evaluation of Stacked Auto-Encoder neural network and Multi-Layer Extreme Learning Machine for detection and classification of faults in transmission lines using WAMS data

Smart grid is envisaged as a power grid that is extremely reliable and flexible. The electrical grid has wide-area measuring devices like Phasor measurement units (PMUs) deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability. The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data. This research proposes a transmission line fault detection and classification (FD&C) system based on an auto-encoder neural network. A comparison between a Multi-Layer Extreme Learning Machine (ML-ELM) network model and a Stacked Auto-Encoder neural network (SAE) is made. Additionally, the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models. With substantially shorter testing time, the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79% accuracy. The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms. To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio (SNR) ranging from 10 dB to 40 dB. The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.

Research on Identification and Detection of Transmission Line Insulator Defects Based on a Lightweight YOLOv5 Network

Transmission line fault detection using drones provides real-time assessment of the operational status of transmission equipment, and therefore it has immense importance in ensuring stable functioning of the transmission lines. Currently, identification of transmission line equipment relies predominantly on manual inspections that are susceptible to the influence of natural surroundings, resulting in sluggishness and a high rate of false detections. In view of this, in this study, we propose an insulator defect recognition algorithm based on a YOLOv5 model with a new lightweight network as the backbone network, combining noise reduction and target detection. First, we propose a new noise reduction algorithm, i.e., the adaptive neighborhood-weighted median filtering (NW-AMF) algorithm. This algorithm employs a weighted summation technique to determine the median value of the pixel point’s neighborhood, effectively filtering out noise from the captured aerial images. Consequently, this approach significantly mitigates the adverse effects of varying noise levels on target detection. Subsequently, the RepVGG lightweight network structure is improved to the newly proposed lightweight structure called RcpVGG-YOLOv5. This structure facilitates single-branch inference, multi-branch training, and branch normalization, thereby improving the quantization performance while simultaneously striking a balance between target detection accuracy and speed. Furthermore, we propose a new loss function, i.e., Focal EIOU, to replace the original CIOU loss function. This optimization incorporates a penalty on the edge length of the target frame, which improves the contribution of the high-quality target gradient. This modification effectively addresses the issue of imbalanced positive and negative samples for small targets, suppresses background positive samples, and ultimately enhances the accuracy of detection. Finally, to align more closely with real-world engineering applications, the dataset utilized in this study consists of machine patrol images captured by the Unmanned Aerial Systems (UAS) of the Yunnan Power Supply Bureau Company. The experimental findings demonstrate that the proposed algorithm yields notable improvements in accuracy and inference speed compared to YOLOv5s, YOLOv7, and YOLOv8. Specifically, the improved algorithm achieves a 3.7% increase in accuracy and a 48.2% enhancement in inference speed compared to those of YOLOv5s. Similarly, it achieves a 2.7% accuracy improvement and a 33.5% increase in inference speed compared to those of YOLOv7, as well as a 1.5% accuracy enhancement and a 13.1% improvement in inference speed compared to those of YOLOv8. These results validate the effectiveness of the proposed algorithm through ablation experiments. Consequently, the method presented in this paper exhibits practical applicability in the detection of aerial images of transmission lines within complex environments. In future research endeavors, it is recommended to continue collecting aerial images for continuous iterative training, to optimize the model further, and to conduct in-depth investigations into the challenges associated with detecting small targets. Such endeavors hold significant importance for the advancement of transmission line detection.

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines.

With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance

In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for concurrently detecting and classifying several faults using a multilabel classification approach based on binary relevance classifiers. The proposed binary relevance multilabel detection and classification models’ performances are examined. Under both ideal and problematic circumstances, faults in the dataset are collected. A variety of multilabel fault types detection and classification determines the suggested method’s effectiveness.