Transmission Line Fault Detection Research Articles

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

The Use of Artificial Neural Network for Low Latency of Fault Detection and Localisation in Transmission Line

The Use of Artificial Neural Network for Low Latency of Fault Detection and Localisation in Transmission Line

Automatic Feature Learning-Based Fault Detection and Classification in Power Transmission Lines Using Deep Transfer Model

Automatic Feature Learning-Based Fault Detection and Classification in Power Transmission Lines Using Deep Transfer Model

An overview of advanced technologies implemented for overhead transmission line fault detection

An overview of advanced technologies implemented for overhead transmission line fault detection

Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

Framework for Transmission Line Fault Detection in a Five Bus System Using Discrete Wavelet Transform

The transmission line is an essential part of the power system and since itis exposed to the harsh environmental conditions, it is susceptible to opencircuit and short circuit faults. As the occurrence of failures affect the qualityof the electric power supplied, identifying the fault and clearing it quickly iscritical for the reliable power system operation. Fault Diagnostics of a multibus system is crucial for Power system stable operation. In this paper, a five-bus system is taken up for the study and twelve different types of faults withvarying fault durations are applied in transmission lines at different locations.Then Discrete Wavelet Transform (DWT) is applied to the receiving endcurrent signals of the line and derived wavelet coefficients are used to identifythe type of fault. Simulation and extraction of wavelet coefficients are done in MATLAB Simulink and results are presented. The Model Developed of a fiveBus system for Fault Diagnostics and Simulation can be used as Frameworkfor Fault Classification and Localization. The derived Wavelet Transformproves to be appropriate for the Framework developed.

Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults

Timely and accurate detection of transmission line faults is one of the most important issues in the reliability of the power systems. In this paper, in order to assess the effects of impedance and location of the fault in identifying and classifying it, the frequency response analysis (FRA) method is utilized. This method clearly shows the smallest effects of the faults on voltage and current signals in the frequency domain. Interpretation of the results associated with the FRA procedure is considered a weakness of this method. To overcome this issue and accurately categorize types and locations of various transmission lines faults such as asymmetric faults and symmetric faults, machine learning, and deep learning applications called support vector machine (SVM), decision tree (DT), k-Nearest Neighbors (k-NN), convolutional neural network (CNN), long short term memory (LSTM), and a hybrid model of convolutional LSTM (C-LSTM) are utilized. Introduced faults are applied with various impedances in 6 segments of an IEEE standard transmission line system. Then, the frequency response curves (FRCs) for them are computed and selected as input datasets for the suggested networks. After categorizing the types and locations of faults, the results for each network are analyzed via different statistical performance evaluation metrics. Finally, in order to early detection of faults, the new high impedance faults (7000 and 9000 O) are applied based on the previous routine in the transmission line. At this stage, evaluations demonstrate the capability of the C-LSTM followed by SVM, DT, k-NN, CNN, and LSTM in categorizing the type and location of transmission line faults.

Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

End to end machine learning for fault detection and classification in power transmission lines

This paper proposes a new machine learning approach for fault detection and classification tasks in electrical power transmission networks. This method exploits the temporal sequence of the power system's operational data and develops an ‘end to end’ model employing Long Short-Term Memory (LSTM) units working directly on the operational data instead of features. The temporal sequences are different in the case of normal and faulty conditions. End to end learning simplifies the decision-making process and eliminates the need for complex feature extraction process by learning directly from the labelled datasets. The method is rigorously tested for all types of faults, which are further subjected to a range of fault resistance, distance, loading conditions, system parameters and noise levels. The proposed method can also work under power swing conditions. The method is also tested on WSCC 9 bus system. The proposed method has shown fast response in terms of time performance and is resilient towards operational conditions.