Transmission Line Fault Research Articles

Monitoring UAV status and detecting insulator faults in transmission lines with a new classifier based on aggregation votes between neural networks by interval type-2 TSK fuzzy system

Monitoring UAV status and detecting insulator faults in transmission lines with a new classifier based on aggregation votes between neural networks by interval type-2 TSK fuzzy system

Line to Ground Fault Detector in 400 KV Transmission Line by Using Intelligent System

The ability to locate and identify problems in power transmission lines is the most crucial component of power system engineering since it ensures the stable and efficient operation of electrical grids. to minimize downtime, prevent cascading failures, and maintain the power system's stability. problem localization helps identify the precise location of the problem and speeds up the process of restoring the power supply while minimizing the impact on consumers. This paper presents a measurement-based automatic fault localization and detection (ANFIS) technique for transmission lines. High-speed processing of real-time error localization and detection is made possible by the design and implementation of ANFIS. It is suggested that ANFIS for digital distance protection systems be able to discover issues in addition to detecting them. When a transmission experiences a three-phase malfunction Thus, the suggested method can precisely pinpoint the impacted stages. ANFIS has been trained and tested using many kinds of field data. The field data are collected by simulating faults in the Simulink Matlab model depicting the transmission line at various sites between Misan – Kut station 400 Kv along 200 Km using computer software based on Matlab. Phase current and voltage measurements are utilized as ANFIS inputs and are available at the busses. Regarding the kind and detection of defects, The output will demonstrate fault and location identification with an extremely low error percentage through simulated processes; the results also demonstrate that the approach's selectivity and speed are quite dependable and provide adequate performance for applications involving transmission and distribution monitoring; and security. The study emphasizes the criticality of promptly and precisely locating faults in power transmission lines to preserve the stability and security of the power system.

Fault Diagnosis and Localization of Transmission Lines Based on R-Net Algorithm Optimized by Feature Pyramid Network

Timely fault diagnosis and localization of transmission lines is crucial for ensuring the reliable operation of increasingly complex power systems. This study proposes an optimized R-Net algorithm based on a feature pyramid network (FPN) and densely connected convolutional network (D-Net) for transmission line fault diagnosis and localization. The R-Net network is enhanced by reshaping the anchor points using an improved K-means algorithm and incorporating an FPN for multi-scale feature extraction. The backbone network is further optimized using D-Net to strengthen inter-layer connections and improve feature reuse. Experimental results demonstrate that the optimized R-Net achieves an overall average accuracy of 0.64, outperforming the original network by 1.30%. The accuracy improvement is particularly significant for ground wire defects (2.40%). The D-Net-based R-Net, despite having fewer parameters, maintains high accuracy (0.6502). Compared to other object detection algorithms, such as YOLO-v3 and Faster R-CNN, the optimized R-Net exhibits superior performance in terms of mean average precision (15.58% and 2.45% higher, respectively) and parameter efficiency (17M vs. 38M and 81M). Considering both performance and speed, the optimized R-Net achieves a processing rate of 10.5 frames per second. This study provides an efficient and accurate tool for transmission line fault diagnosis and localization, with significant practical implications for power system operation and maintenance.

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.

An efficient deep learning based scheme for adaptive auto-reclosing in power transmission lines

As a large percentage of faults in transmission lines are single-line-to-ground (SLG) and transient in nature, the use of adaptive single-phase auto-reclosing (ASPAR) schemes is essential. In this paper, a new scheme for ASPAR is proposed to improve the stability and reliability of power transmission lines. The proposed approach distinguishes between the transient and permanent faults by using the feature of Stationary Wavelet Transform (SWT) and Gate Recurrent Unit (GRU) deep artificial neural network. Also, by utilizing another GRU network, the proposed scheme predicts the extinction time of the secondary arc (SA) one power cycle before the complete extinction. This can increase the speed of the faulty phase reclosing in the case of a transient fault. Simulation results carried out on a 400 kV power transmission line in the EMTP-RV and MATLAB software environments illustrate that the F1 score of the proposed ASPAR in classifying the faults is 99.55 %, and its average error in predicting the extinction time of the SA based on the root mean square error (RMSE) criterion is 0.0557. Also, the superiority and high-quality performance of the proposed protection scheme are demonstrated by comparing the results with several state-of-the-art methods.

A hybrid energy harvesting approach for transmission lines based on triboelectric nanogenerator and micro thermoelectric generator

To effectively detect faults in transmission lines, monitoring the operating status of these lines is imperative. However, providing power to monitoring devices for transmission line status presents a significant challenge. In this research, a hybrid energy harvesting approach based on micro thermoelectric generator (MTEG) and triboelectric nanogenerator (TENG) is proposed, and a theoretical model for MTEG-TENG hybrid energy harvesting is established. This study develops an integrated energy harvesting prototype, which incorporates oscillating-TENG (O-TENGs), MTEGs, and a power management control unit. This prototype not only harvests energy from the vibrations of transmission lines but also converts the lines thermal energy into electricity. The Experiment results show that the maximum open-circuit voltages of O-TENG and MTEG reach 80.3 V and 1.094 V, respectively. Compared to a single MTEG energy harvesting device, the prototype of the MTEG-TENG hybrid energy harvesting device demonstrates a 5.36% improvement in energy harvesting and battery charging performance. Consequently, this approach achieves self-powered monitoring with excellent stability and lower manufacturing costs. It provides an efficient and durable power approach for transmission line status monitoring devices.

Optimization of quenching characteristics of a multi-chamber arrester

Lightning is a major cause of tripping faults in overhead transmission lines. Multi-chamber lightning arresters are widely used to protect high-voltage lines from lightning because of their reliable arc-quenching performance. This paper analyzes the development and quenching process of an arc after a lightning breakdown arrester and studies the quenching mechanism of a multi-chamber arc structure based on its structural characteristics. We built an impulse-current test platform and carried out an impulse-current test for the multi-chamber arrester. The arc motion was captured by a high-speed camera, and the height, speed, and temperature of the arc ejection were obtained by signal processing. The multi-chamber arrester structure is optimized by using the maximum arc ejection height and the maximum ejection speed as metrics to find the optimal combination of the quenching-hole diameter and depth and the gap distance of the multi-chamber structure.

Research on New Energy Transmission Line Fault Localization Method Based on APSVMD-NTEO

Abstract Given the new energy transmission line fault, traveling wave head detection difficulties, wave speed uncertainty, positioning errors, and other problems, this paper proposes a new Teager energy operator based on adaptive parameter selection variational mode decomposition (APSVMD) and new energy transmission line fault location method (NTEO). The envelope fuzzy entropy is introduced to select the key parameters of SVM, and the sampling range of the Teager energy operator (TEO) is improved to effectively improve the capture accuracy of the wave head. In terms of fault location, by improving the double-ended traveling wave method, the uncertainty of wave speed and the effect of actual line length error is eliminated, and the ranging error is reduced.

Partially coupled transmission line fault location using single-ended measurements

Accurate fault location technology can not only speed up fault search but also enhance the power supply reliability. This paper introduces a single-ended fault location framework for single line to ground faults, taking into account the effects of partially coupled lines. The proposed framework relies only on local measurements. Within the proposed framework, the two methods proposed in this paper are based on the impedance line model and the fully distributed parameter line model, respectively. The impedance model based fault location method does not require iteration and has no risk of divergence, while the method based on the fully distributed parameter model has higher accuracy. The estimated results of the impedance-based method can be used as the initial values of the solution of the distributed parameter model based method. The proposed methods are validated through a number of testing cases, encompassing various fault locations, resistances, and coupling ratios.

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

AbstractFault 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.

Smart Fault Detection on Transmission Lines via IoT Technology

In today's society, the reliability of electricity supply is paramount for sustaining essential services and infrastructure. However, power transmission networks, crucial for distributing electricity over vast distances, are vulnerable to faults that can cause equipment damage and service disruptions. Rapid fault detection is crucial to minimize these risks. This paper proposes an innovative IoT-based solution for detecting faults in transmission lines. The system integrates a center-tapped transformer and switches to simulate fault conditions, allowing real-world testing of fault detection mechanisms. Voltage signals from the transmission line are meticulously analyzed using opto-couplers and a micro-controller, which compare these signals with predefined references. Upon fault detection, immediate notifications are displayed on an LCD and transmitted via a WiFi-enabled IoT system for swift action. Furthermore, all data is logged on a central server for comprehensive analysis and ongoing monitoring. This research endeavors to significantly enhance the reliability and efficiency of transmission line operations by implementing advanced fault detection technologies, thereby reinforcing the resilience of electricity supply networks essential for modern society. Key Words: IoT,WiFi Module,Micro-controller,Transmission line.

Design of Series-Connected Novel Large-Scale Offshore Wind Power All-DC System with Fault Blocking Capability

The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is extremely rapid, with a wide-ranging impact, and to date, there are no complete DC engineering references available. It is crucial to research the topology and fault isolation methods applicable to large-scale offshore wind power all-DC systems in deep-sea areas. This paper proposes a novel series-connected all-DC system topology and presents corresponding fault isolation methods for internal faults in wind turbine units and faults in high-voltage DC transmission lines. The system simulation model was constructed using PSCAD/EMTDC (v4.6.3), and simulations were conducted for internal faults in the wind turbine units and DC transmission line short-circuit faults. The simulation results demonstrate that the proposed system can isolate various DC faults while maintaining stable operation, thereby validating the effectiveness of the control strategies and fault isolation methods proposed in this paper.

Contingency detection in modern power systems: A stochastic hybrid system method

This paper introduces a new stochastic hybrid system (SHS) framework for contingency detection in modern power systems (MPS). The framework uses stochastic hybrid system representations in state space models to expand and facilitate capability of contingency detection. In typical microgrids (MGs), buses may contain various synchronous generators, renewable generators, controllable loads, battery systems, regular loads, etc. For development of SHS models in power systems, this paper introduces the concept of dynamic and non-dynamic buses. By converting a physical power grid into a virtual linearized state space model and representing contingencies as random switching of system structures and parameters, this paper formulates the contingency detection problem as a joint estimation problem of discrete events and continuous states in stochastic hybrid systems. This method offers unique advantages, including using common measurement signals on voltage and current synchrophasors to detect different types and locations of contingencies, avoiding expensive local direct fault measurements and detecting certain contingencies that cannot be directly measured. The method employs a small and suitably-designed probing signal to sustain the ability of persistent contingency detection. Joint estimation algorithms are presented with their proven convergence and reliability properties. Examples that use an IEEE 5-Bus system demonstrate the main ideas and derivation steps. Simulation case studies on an IEEE 33-Bus system are used for detecting transmission line faults and sensor interruptions.

UHVDC transmission line diagnosis method for integrated community energy system based on wavelet analysis

With the large-scale development of renewable energy power, China has faced with the challenges of the reverse regional distribution of wind and solar resources and power load, as well as the intermittency and randomness of renewable energy power. Therefore, China is vigorously developing ultra-high voltage direct current (UHVDC) transmission technology to solve the problem of insufficient flexibility caused by the uncertainty of renewable energy and the fluctuation of multi-energy loads in integrated community energy systems. UHVDC plays an increasingly pivotal role in the west-east transmission system in China’s power system due to its high transmission capacity and long transmission distance. Once the fault occurs in the ultra-high voltage direct (UHVD) transmission line, quick and accurate fault location identification is of great significance. Hence, this paper proposes a UHVDC transmission line diagnosis method based on wavelet analysis for integrated community energy systems. Wavelet transform (WT) is used to decompose the transient signal on a multi-scale, and then power systems computer-aided design (PSCAD) software is utilized for simulation calculation to obtain the singular spectrum entropy of each layer and facilitate wavelet transformations for signal denoising with advanced tools such as MATLAB. The prediction results can distinguish outside the rectification side fault, within the rectification side fault, and outside the inverter fault with an accuracy of 100%. A large number of simulations demonstrate that combining singular spectrum entropy with support vector machines (SVM) has emerged as a robust technique for integrated community energy systems, suggesting its potential as a standard method in UHVDC transmission line diagnosis. This study is of significant reference for realizing the complementarity of multiple types of power supply and ensuring a reliable power supply.

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy.

A robust unified data-driven protection scheme for hybrid AC/DC transmission lines

To meet rising energy demands, reduce carbon emissions, and address climate change, the importance of hybrid AC/DC transmission has increased. The effective utilization of high-voltage DC in existing AC transmission lines will reduce costs and losses and increase the existing transmission capacity. Therefore, the protection schemes of fault detection, location, and classification are complicated because of the different characteristics of AC and DC in the same transmission network. The existing protection schemes mostly utilize threshold-based criteria and cannot ensure hybrid network protection. To overcome these challenges, a unified data-driven protection scheme (UDDPS) using distinctive features is proposed to detect and identify (either AC or DC) and classify both AC/DC faults of hybrid transmission lines (HTLs). The UDDPS has ensured the protection of AC/DC lines by timely intervening faults and effectively isolating the respective relays of the faulty lines. The fault current data are retrieved during a fault abnormality using distinctive retrieved features from the single end of the HTL. The proposed scheme applies to multi-transmission networks and is independent of the communication channels. Extensive simulation scenarios, such as fault/no-fault, varying fault locations, and near and far locations with low/high impedances, were examined in MATLAB/Simulink to train the UDDPS. The UDDPS successfully detected and identified hybrid AC/DC line faults with 100% accuracy and further classified the AC and DC faults with 97.61 % and 99.20 % accuracy. The efficacy and robustness of the proposed technique were confirmed by comparison with the existing schemes of support vector machine, decision tree, and LSTM. The UDDPS surpassed in terms of the overall model under a normal and noisy environment.

Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines

In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems.

Implementing generative adversarial networks for increasing performance of transmission fault classification

<p>An electrical power system is a network that facilitates the sourcing, transfer, and distribution of electrical energy. In the traditional power system, there are eleven types of faults that can occur in the system. This paper focuses on the classification of these faults over a stretch of 100 kilometres. The dataset used is synthetic and generated from a simulated model using MATLAB/Simulink software. Data augmentation is carried out during training to improve the accuracy of the classification. An indirect training approach through generative adversarial network (GAN) is used to classify these overhead transmission line faults. The random forest (RF) classification is used as the base learning model on the original dataset and it achieves accuracy of 84%. However, the base learner RF when used on GAN model generated augmented faulty data, it performs exceptionally well achieving 99% accuracy. One of the recent state-of-art methods is compared with this approach.</p>