Transmission Line Fault Classification Research Articles

Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier

Transmission line fault classification forms the basis of fault protection management in power systems. Because faults have adverse effects on transmission lines, adequate measures must be implemented to avoid power outages. This paper focuses on using the categorical boosting (CatBoost) algorithm classifier to analyse and train multiple voltage and current data from a 330 kV and 500 km-long simulated faulty transmission line model designed using Matlab/Simulink. From it, 93,340 fault data sizes were extracted. The CatBoost classifier was employed to classify the faults after different machine learning algorithms were used to train the same data with different parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46% for 748 iterations out of 1000. The algorithm was selected for its high performance in classifying faults based on accuracy, precision and speed. In addition, it is easy to use and handles multiple data-sets. In contrast, a support vector machine and an artificial neural network each has a longer training time than the proposed method’s 58.5 s. Proper fault classification techniques assist in the effective fault management and planning of power system control thereby preventing energy waste and providing high performance.

Transmission Line Fault Location and Classification Using Machine Learning Technique

Transmission Line Fault Location and Classification Using Machine Learning Technique

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.

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

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

Fault Identification on Electrical Transmission Lines Using Artificial Neural Networks

Transmission lines are built to span over long distances, to which they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. Accurate fault classification and location is essential in reducing outage times and enhancing system reliability. Diverse methods exist and have different strengths and weaknesses. This paper aims to investigate the use of an intelligent technique based on artificial neural networks. The neural networks will attempt to determine the fault classification and precise fault location. Different fault cases are analyzed on multiple transmission line configurations using various phasor measurement arrangements from the two substations connecting the transmission line. The results can provide guidance on choosing the most efficient neural network structure and input measurements for transmission line fault classification and location.

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.

An Ultra-High-Speed Algorithm for Fault Classification in Double Circuit Transmission Lines Using Only the First Group of Received Current Traveling Waves

This paper presents a new and very fast fault detection (FD), fault classification (FC), and fault phase selection (FPS) method, which is just based on the first group of current traveling waves (cTW) in double circuit transmission lines (DCTLs). All internal, inter-circuit faults and lightning strikes on transmission line (TL) phases and shield wires are reviewed. By the proposed algorithm, FC and FPS are performed in the shortest possible time. In this study, wavelet transform (WT) is used to extract the traveling wave (TW) caused by the fault from the current signal of one DCTL terminal, and only the first group of these waves is used for FC and PFS to increase velocity. To classify various fault types, the zero components of TWs of the two lines, which can be calculated using the conventional modal transforms and another modal transform of 6*6 dimensions defined on all six phases of the DCTL, is used. All simulations are performed in PSCAD on a 230kV grid and a DCTL of 200km length. Also, the performance of the proposed algorithm is tested under various fault and lightning conditions in the presence of white noise with different incidence angles and various fault resistances.

Particle swarm optimization pattern recognition neural network for transmission lines faults classification

The operating conditions of the transmission lines can be assessed through the information measured by the smart meters in the power supply bureau. Accurate classification of transmission line faults can be helpful to improve the maintenance strategy of smart grids. This paper analyzes the mechanism of the voltage loss and the phase fault of the transmission line by using the operation data collected by the smart meters from three power supply bureaus (named Bureau A, B and C), where the faults are labeled by expert systems. In this work, a novel Particle Swarm Optimization Pattern Recognition Neural Network (PSO-PRNN) classifier is built to accurately categorize the faults and its classification performance is compared with the ones of traditional K-Nearest Neighbor (KNN), Decision Tree (DT), PSO-KNN and PSO-DT classifiers. The results show that the classification accuracy of PSO-PRNN outperforms traditional classifiers when being applied to the data collected from all three bureaus. In the A power supply bureau are 83.0%, 88.7%, 82.0%, 86.9% and 96.1%, and the classification accuracy rates are 55.7%, 68.7%, 56.6%, 68.7% and 82.5%, when used to process the data of the bureau B. The classification accuracy is 57.1%, 66.4%, 57.2%, 69.0% and 82.1%, when processing the data of bureau C. The results show that the PSO-PRNN classifier is superior to the others in terms of accuracy and applicability.

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.

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

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

Transmission Line Fault Detection and Classification Based on Sa-Mobilenetv3

Transmission Line Fault Detection and Classification Based on Sa-Mobilenetv3

Catboost Algorithm for Transmission Line Faults Detection and Classification

Catboost Algorithm for Transmission Line Faults Detection and Classification

Fault Classification in Transmission Lines Using Random Forest and Notch Filter

Fault Classification in Transmission Lines Using Random Forest and Notch Filter

Fault classification and phase selector algorithm for half-wavelength transmission lines

This study presents a new phase selector algorithm and fault type classification for very long transmission lines, known as the half-wavelength lines (HWL). For this work, the Clarke components variation rate method applied to current magnitudes was used. The method was implemented in a real-time digital simulation hardware-in-the-loop (HIL) test environment. We applied different fault types at each 5% line length, obtaining Clarke component current phasors. The proposed algorithm is stable for the native behavior of the half-wavelength line, including the resonance zones and their mitigation procedures. In addition, the algorithm is immune to the fault insertion angle, fault resistance, and line loading level. Finally, the algorithm could be applied in commercial microprocessor-based relays or could be used as a post-fault analysis tool.

Comparison and Analysis of the Influence of Different Data Transformation Methods on the Fault Identification of Flexible DC Transmission Lines by Convolutional Neural Network

In the fault classification and identification of flexible DC transmission lines, it is inevitable to use the voltage and current characteristics of the transmission line. All kinds of data transformation methods can highlight the hidden characteristics of the original fault electrical quantity. Various artificial intelligence algorithms can further reduce the difficulty of transmission line fault classification. For such fault classification methods, this paper first builds a four-terminal flexible direct current transmission system model on PSCAD/EMTDC platform and obtains data by simulating different faults of transmission lines. Then, empirical mode decomposition (EMD), wavelet transform (WT), fast Fourier transform (FFT), and variational mode decomposition (VMD) are performed on the obtained data, respectively. Finally, the transformed data and original data are used as inputs to classify by convolutional neural network (CNN). The influence of one data transformation method and different combinations of two data transformation methods on CNN classification results is explored. The simulation results show that when only one data transformation method is used, CNN has the best classification effect for the data after VMD transformation. The classification accuracy and recall rate are both increased from 96.9% and 96.3% without data transformation to 99.88%. When VMD and FFT are combined, CNN classification results’ accuracy and recall rate are further improved to 99.96%.

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>

Application of Poincaré analogous time-split signal-based statistical correlation for transmission line fault classification

Application of Poincaré analogous time-split signal-based statistical correlation for transmission line fault classification

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.

A deep learning based intelligent approach in detection and classification of transmission line faults

Detection and classification of transmission line (TL) faults are key factors for the fault root cause analysis and rapid restoration of the power network. Deep learning can extract representative features automatically from big data, thus becoming an important breakthrough in fault analysis. Existing deep learning-based fault diagnosis models rely on a large number of data obtained from a variety of fault conditions to be generalized. Indeed, in the TL domain, it is difficult to define and, therefore to collect the fault incepted features of all possible fault scenarios. This paper proposes an unsupervised framework for fault detection and classification (FDC) of TL based on a capsule network (CN). Instead of using the baseline CN, an extension to this with a sparse filtering technique is adopted in this work. The capsule network with sparse filtering (CNSF) voluntarily learns the expensive fault features and significantly improves the model performance without involving a large number of data. The proposed scheme receives 1/2 cycle post-fault three-phase signals and encodes them into a single image that is defined as the input for the proposed CNSF model. The effectiveness of the proposed CNSF model is corroborated by four different TL topology confirming the model’s adaptability to a topology change in response to intended control action or the switching actions due to cascading faults. Further assessment of the model’s performance against noise, high impedance faults (HIF), and line parameter variations are also carried out in order to confirm the high reliability of the proposed model. In addition, a rigorous comparative study is conducted to guarantee the state-of-the-art performance of the proposed model.