Transmission Line Fault Detection Research Articles

Hierarchical Temporal Memory (HTM)Approach for Fault Detection in Transmission Line

Hierarchical Temporal Memory (HTM)Approach for Fault Detection in Transmission Line

Automated Transmission Line Fault Detection using Distance Locator

Faults play an important role in affecting the reliability of power system. More than 80% of the power system faults occur in transmission sector which badly effect the reliability of supply and causes damage to the system. So, it is compulsory to monitor the transmission lines in normal and faulty conditions and clear the fault as soon as identified. The main aim of this proposed research is to design a circuit which will have a capability of determining the type of fault and exact location of fault. From the results of simulations, it has been concluded that the four types of faults such as L-G, L-L and L- L-G have been detected with their location remotely. Afterwards, transfer its data to the utility mobile phone of the user with the help of GPS and NodeMCU.

Transmission line fault detection and classification based on SA-MobileNetV3

Accurate fault detection and classification help to analyze fault causes and quickly restore faulty phases. Deep learning can automatically extract fault features and identify fault types from the original three-phase voltage and current signals. However, this still imposes challenges such as recognition accuracy and computational complexity. More importantly, high level fault features cannot be extracted in the one-dimensional time series. This paper presents a robust fault classification method based on SA-MobileNetV3 for transmission systems. Considering that the SE (Squeeze-and-Excitation) attention module cannot aggregate the spatial dimension information on the channel, SA (shuffle attention) module is introduced into MobileNetV3, which can effectively fuse the importance of pixels in different channels and in different locations at the same channel. Also, transforming the time series three-phase voltage and current signals into two-dimensional images based on CWT (continuous wavelet transform) makes the proposed method be similar to image recognition, which can mine high level fault features and classify the faults visually. To verify the effectiveness of the method, a 735kV transmission line model is built for data generation through Simulink. Various kinds of fault conditions and factors are considered to verify the adaptability and generalizability. Simulation results show that the method can quickly and accurately identify 11 types of faults, and the accuracy rate is as high as 99.90%. A comparison between the proposed method and other existing techniques shows the superiority of the proposed SA- MobileNetV3, and better anti-noise performance makes it more suitable for real fault signals taken on-site.

Online fault detection and classification of 3-phase long transmission line using machine learning model

Online fault detection and classification of 3-phase long transmission line using machine learning model

A Robust Fault Diagnosis Scheme using Deep Learning for High Voltage Transmission Line

The transmission lines repeatedly face an aggregation of shunt-faults and its impact in the real time system increases the vulnerability, damage in load, and line restoration cost. Fault detection in power transmission lines have become significantly crucial due to a rapid increase in number and length. Any kind of interruption or tripping in transmission lines can result in a massive failure over a large area, which necessitates the need of effective protection. The diagnosis of faults help in detecting and classifying transients that eventually make the protection of transmission lines convenient. In this paper, we propose a deep learning-enabled technique for the detection and classification of transmission line faults. The faulty information are extracted using Discrete Wavelet Transform (DWT) and fed into the multilayer perceptron classification model. The results indicate that the proposed approach is capable of accurately classifying and detecting faults in transmission line with high precision.

Application of machine learning methods in fault detection and classification of power transmission lines: a survey

Application of machine learning methods in fault detection and classification of power transmission lines: a survey

Development of DSP-Based Transmission Line Fault Detection

Abstract: This paper proposes a new algorithm to detect and classify faults in the electric power transmission line. Transmission line protection is an important issue in power system engineering because 85-87% of power system faults are occurring in transmission lines. This paper presents a technique to detect and classify the different shunt faults on transmission lines for quick and reliable operation of protection schemes. Discrimination among different types of faults on the transmission lines is achieved by the application of evolutionary programming tools. Further, fault signal data are imported into MATLAB for postprocessing, and time-frequency analysis using Signal Processing Toolbox in MATLAB.

Uncertainty awareness in transmission line fault analysis: A deep learning based approach

With the expansion of the modern power system, it is of increasing significance to analyze the faults in the transmission lines. As the transmission line is the most exposed element of a power system, it is prone to different types of environmental as well as measurement uncertainties. This uncertainties influence the sampled signals and negatively affects the fault detection and classification performance. Therefore, an unsupervised deep learning framework named deep belief network is presented in this paper for fault detection and classification of power transmission lines. The proposed framework learns the beneficial feature information from the uncertainty affected signals with a unique two stage learning strategy. This strategy enables the proposed framework to extract lower level fault-oriented information which may remain unobserved for other alternative approaches. The efficacy of the proposed framework has been examined on the IEEE-39 bus benchmark topology. The in-depth accuracy assessment with different accuracy metrics along with exclusive case studies such as the influence of noise, measurement error as well as line and source parameter variations will be conducted in this paper to justify the real-world applicability of the proposed framework. Furthermore, the relative performance assessment with the cutting-edge rival techniques is also presented in this paper to verify if the proposed framework attains a state-of-the-art classification performance or not.

Fault Detection and Classification in Transmission Lines Connected to Inverter-Based Generators Using Machine Learning

Integrating inverter-based generators in power systems introduces several challenges to conventional protection relays. The fault characteristics of these generators depend on the inverters’ control strategy, which matters in the detection and classification of the fault. This paper presents a comprehensive machine-learning-based approach for detecting and classifying faults in transmission lines connected to inverter-based generators. A two-layer classification approach was considered: fault detection and fault type classification. The faults were comprised of different types at several line locations and variable fault impedance. The features from instantaneous three-phase current and voltages and calculated swing-center voltage (SCV) were extracted in time, frequency, and time–frequency domains. A photovoltaic (PV) and a Doubly-Fed Induction Generator (DFIG) wind farm plant were the considered renewable resources. The unbalanced data problem was investigated and mitigated using the synthetic minority class oversampling technique (SMOTE). The hyperparameters of the evaluated classifiers, namely decision trees (DT), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Ensemble trees, were optimized using the Bayesian optimization algorithm. The extracted features were reduced using several methods. The classification performance was evaluated in terms of the accuracy, specificity, sensitivity, and precision metrics. The results show that the data balancing improved the specificity of DT, SVM, and k-NN classifiers (DT: from 99.86% for unbalanced data to 100% for balanced data; SVM: from 99.28% for unbalanced data to 99.93% for balanced data; k-NN: from 99.64% for unbalanced data to 99.74% for balanced data). The forward feature selection combined with the Bag ensemble classifier achieved 100% accuracy, sensitivity, specificity, and precision for fault detection (binary classification), while the Adaboost ensemble classifier had the highest accuracy (99.4%), compared to the other classifiers when using the complete set of features. The classification models with the highest performance were further tested using a new dataset test case. They showed high detection and classification capabilities. The proposed approach was compared with the previous methodologies from the literature.

PSO driven designing of robust and computation efficient 1D-CNN architecture for transmission line fault detection

In spite of the amazing success of deep learning in various complex classification tasks, its real-time applications have been greatly hindered due to the enormous computational burden and extensive processing time. In this context, the present investigation is a path-breaking idea and journey toward designing robust and computationally efficient One-dimensional Convolutional Neural Networks (1D-CNN) for the classification of transmission line faults, which demands accurate decisions within 20 ms. Following the multi-channel signal processing approach, sampled three-phase line currents of each half-cycle duration are concatenated to form a one-dimensional array and fed to the 1D-CNN model for exploring unique patterns of line currents associated with various faults. A Particle Swarm Optimization (PSO) driven multi-objective optimization technique is exclusively used to select an optimum number of kernels and kernel sizes for designing a less computation-intensive but robust CNN architecture, making it suitable for fast fault detection and classification without compromising the performance. The efficacy of this holistic design approach has been demonstrated in the simulated environment of an IEEE 5-bus system with significant penetration of distributed generation. With excellent fault pattern extraction through the convolution process, the optimally designed 1D-CNN has proven its strength for accurate transmission line fault classification by combating bidirectional power flow due to renewable energy penetration. The emulation time of the optimally designed 1D-CNN architecture for low-cost, low-power FPGA demonstrates its fastness and suitability for time-critical real-time transmission line fault classification. In general the proposition in the paper explains the candidate Deep Learning (DL) algorithm for multi-channel signal processing focusing on low-computing edge devices.

Deep learning for component fault detection in electricity transmission lines

Component fault detection and inventory are one of the most significant bottlenecks facing the electricity transmission and distribution utility establishments especially in developing countries for delivery of efficient services to the customers and to ensure proper asset audit and management for network optimization and load forecasting. For lack of technology and data, insecurity, the complexity associated with traditional methods, untimeliness, and general human cost, electricity assets monitoring, and management have remained a big problem in many developing countries. In view of this, we explored the use of oblique UAV imagery with high spatial resolution and fine-tuned deep Convolutional Neural Networks (CNNs) for automatic faulty component inspection and inventory in an Electric power transmission network (EPTN). This study investigated the capability of the Single Shot Multibox Detector (SSD), a one-stage object detection model on the electric transmission power line imagery to localize, detect and classify faults. Our proposed neural network model is a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults through a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision (mAP) of 89.61%. All the developed SSD-based models achieve a high precision rate and low recall rate in detecting faulty components, thus achieving acceptable balance levels of F1-score and representation. We have established in this paper that combined use of UAV imagery and computer vision presents a low-cost method for easy and timely electricity asset inventory, especially in developing countries. This study also provides the guide to various considerations when adopting this technology in terms of the choice of deep learning architecture, adequate training samples over multiple fault characteristics, effects of data augmentation, and balancing of intra-class heterogeneity.

Traveling Wave-based Algorithm for Fault Detection, Classification, and Location in STATCOM-Compensated Parallel Transmission Lines

The injection or absorption of reactive power by the static synchronous compensator (STATCOM) may lead to the misoperation of protective relays and single-phase-tripping systems that operate based on impedance measurement. Also, high-frequency harmonic components caused by switching the internal circuitry of a STATCOM lead to unintended tripping of relays that work based on traveling waves. This paper proposes a single-terminal approach to detect, locate, and identify the faulty line(s) and phase(s) in parallel transmission lines equipped with STATCOMs using traveling waves. To that end, these processes were designed so that adding a STATCOM or different operating modes does not degrade their efficiency and accuracy. The faults and faulty phases were simultaneously detected using the first set of traveling waves received at the sending end of the parallel lines at a sampling frequency of 200 kHz. Furthermore, a sampling frequency of 1 MHz was used briefly to improve the fault location accuracy. The discrete wavelet transform (with BD4 mother wavelet) was adopted to extract the traveling waves. All simulations and analyses were conducted in the PSCAD environment. The results obtained from numerous simulations at different places and states of the networks verified the high accuracy and validity of the proposed methods.

Generalized fault diagnosis method of transmission lines using transfer learning technique

Recent artificial intelligence-based methods have shown great promise in the use of neural networks for real-time detection of transmission line faults and estimation of their locations. The expansion of power systems including transmission lines with various lengths have made the fault detection, classification, and location estimation process more challenging. Transmission line datasets are stream data which are continuously collected by various sensors and hence, require generalized and fast fault diagnosis approaches. Newly collected datasets including voltages and currents for faulty and non-faulty situations might not have adequate and accurate labels that are useful to train neural networks. In this paper, a novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. This method is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source convolutional neural network to predict a dissimilar target dataset. By transferring this knowledge, faults from various transmission lines, even without sufficient data samples with labels, can be diagnosed faster and more efficiently than the existing methods. To prove the feasibility and effectiveness of this methodology, seven different datasets that include various lengths of transmission lines are used. The robustness of the proposed methodology against the generator voltage fluctuations, variations in fault locations, fault inception angle, fault resistance, and phase difference between the two generators are well studied to prove the reliability of this technique for fault diagnosis of transmission lines.

Smart Technology Based Empirical Mode Decomposition (EMD) Approach for Autonomous Transmission Line Fault Detection Protection

Many novel technologies of property energy and cell, solar power, batteries, and high-efficient combustion are widely investigated to conserve energy and reduce emissions. Transmission lines (TLs) play a serious role in transmitting generated electricity to different distribution units in facility engineering. The transmission lines function as a link between shoppers and a Power Station. Faults usually occur within the transmission when positioned in an open field. Quick identification and sick line faults square measures required for the conventional operation of the plant. A way like distinct moving ridge rework (DWT) and (EMD) is used to locate and identify faults to resolve this disruption. DWT is used to break down fault transients, as a result of which the info can be collected at the same time in each time and frequency domain. EMD decomposes the TLs voltage into Intrinsic Mode operation (IMFs). Four varieties of fault signals are square measurements produced by the grid-connected facility. Line faults square measure induced MATLAB/Simulink mistreatment.

Fault Detection in Three-Phase Transmission Line by Using IoT

Abstract: The electrical energy device is cut up into many special sections. One in all is the transmission mechanism, wherein Power is transmitted from producing stations and substations through transmission strains to customers. Both strategies Should numerous kinds of malfunctions are generally stated as a "Fault" Fault is described as a number of unwanted however Unavoidable incidents can temporarily disturb the strong circumstance of the power system that occurs while the insulation of the System fails at any factor. Furthermore, if a carrying out object comes in contact with a bare power conductor, a quick circuit, or Fault. The causes of faults are, lighting stroke, human error, wind damage, timber falling or wines plants throughout transmission Strains, aircraft crash on the transmission line, birds shorting traces, or vandalism. At some point in this study, the causes and consequences of faults Within the overhead transmission strains have been the maximum target of the research. A variety of the numerous reasons for faults and some Detection strategies are going to be mentioned. Those faults cause goodsized damage to the power system equipment. In India It's ordinary, the faults might be LG (Line to ground), LL (Line to Line), or 3L (3 lines) inside the availability structures and those faults in 3 phase delivery can affect the strength plant. Keywords: Three-phase Fault, Node MCU, Transmission line, Line to Lind fault, Line to Ground Fault, IoT based.

Euclidean Distance-Based Method for Fault Detection and Classification in Transmission Lines

Euclidean Distance-Based Method for Fault Detection and Classification in Transmission Lines

A novel time-domain method for fault detection and classification in VSC-HVDC transmission lines

The short circuit fault current is one of the worst contingencies in voltage source converter (VSC)-based high voltage direct current (HVDC) system. Therefore, having a fast, robust, and selective protection scheme is necessary in HVDC systems. In this paper, a non-communication-based protection scheme is presented using Intrinsic Time Decomposition (ITD) technique in a HVDC system. This time domain approach owns some prominent features such as high speed, low sampling rate and computational burden and no need for parameter's setting. The proposed protection scheme relies on the current signal sampled from the single end. The concept is to obtain the proper rotation component (PRC) of input signal and computing an energy index named Teager Kaiser Energy Operator (TKEO) to identify the fault occurrence in a HVDC line. The performance of the proposed scheme has been assessed against various scenarios including external disturbance such as ac faults and abrupt change of active power in two standard sample systems in MATLAB/SIMULINK and PSCAD/EMTDC environments. In addition, a wide range of internal dc faults are studied to evaluate the sensitivity of the scheme. The appropriate performance of the scheme is verified and the fault detection and classification tasks are done in less than 1 ms.

Effect of fault classification and detection in transmission line using wavelet detail coefficient

This paper presents the progress of fault detection and classification analysis in transmission line using wavelet transform with high-speed protective digital relay. These techniques are applicable for real time data analysis. The MATLAB simulation model obtains the voltage and current signals that have been measured in both end of the transmission line. These signals are used in a discrete wavelet transform to extract the original signals and to measure the sharp variation of multi-resolution analysis (MRA). The analysis for various types of faults in transmission lines is detected and classified with the help of power protection techniques. In this paper, the wavelet transform technique is used for the detection of faults. This methodology delivers signals in the time and frequency domain. The wavelet transform will be examined to determine the effect of the detail coefficients db-3 and db-4 and provide an acquired time and frequency signal. The performance of wavelet transform will be more suitable, effective, and adequate for high impedance faults in high voltage transmission lines. The power transmission lines are interconnected with the generating station at 400 kv via consumer load through 300 km distance to obtain by MATLAB/Simulink.

Transmission Line Fault Detection and Classification Based on Sa-Mobilenetv3

Transmission Line Fault Detection and Classification Based on Sa-Mobilenetv3

Effect of fault classification and detection in transmission line using wavelet detail coefficient

Effect of fault classification and detection in transmission line using wavelet detail coefficient