Power Transmission Line Fault Research Articles

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.

Advanced Fault Detection in Power Systems Using Wavelet Transform: SIMULINK-Based Implementation and Analysis

Traditional methods struggle to find faults in power transmission lines. This paper presents an approach for short transmission lines, leveraging the power of wavelet transforms. Traditional methods analyze time-domain signals, limiting their ability to differentiate fault transients. Wavelet transforms, offering a combined time-frequency analysis, provide a deeper understanding of these transients. A detailed short transmission line model is built in SIMULINK. Diverse fault scenarios are meticulously simulated, and current signals undergo wavelet transform analysis. Key features extracted from the wavelet coefficients act as fingerprints of potential faults. These features are then utilized to develop a robust fault detection algorithm specifically designed for short transmission lines. The proposed method promises enhanced fault detection capabilities compared to existing techniques in this domain. The results, presented in subsequent sections, will shed light on the effectiveness of wavelet transforms in empowering smarter and more reliable transmission line operations.

Integrative Fault Analysis of Transmission Lines using MATLAB and ETAP

This research paper investigates the major issue of power transmission line faults in electrical engineering. Through advanced simulation tools such as MATLAB and ETAP, the study aims to comprehensively analyze various fault circumstances, including symmetrical and unsymmetrical faults, to determine fault Kilovolt Ampere (KVA) or Megavolt Ampere (MVA). Through detailed fault analysis, insights are sought into improving the reliability and efficiency of power transmission networks, consequently minimizing risks of property damage, casualties, and economic disruptions.

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

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

Eliminating the Dependence of GPS or Communication Latency Estimation in Traveling Wave Based Double-Terminal Fault Location

Double-terminal traveling wave-based fault location is one of the most reliable and accurate technologies for power transmission line fault location. However, existing methods require a global positioning system (GPS) or accurate communication latency estimation to synchronize data from the two terminals. In this study, we propose an innovative double-terminal traveling wave-based fault location method that is independent of either GPS or communication latency estimation. The basic principle of the proposed method is that the arrival time difference between initial surges could be roughly calculated through the angle of pre-fault phasors by artificially assigning surge arrival points as the time references. Therefore, an approximate fault distance is obtained. The error could be reduced by detecting the fault-point-reflected surge. Extensive simulation experiments were performed using PSCAD/EMTDC software to assess the performance of the proposed algorithm. The results showed that the proposed method is applicable to different fault circumstances and independent of GPS synchronization and communication latency estimation while maintaining accuracy close to those of state-of-the-art algorithms. Finally, hardware experiments were performed to assess the validity and practicability of the proposed method.

Power Transmission Line Fault: S-Transform based Feature Extraction and ANN-based Classification

In this paper a new technique for the identification and discrimination of faults on the transmission line of the power system has been presented. The power system network considered in this study is the two-generator system simulated in a Matrix laboratory i.e., MATLAB milieu. Firstly, captured voltage and current are transformed using S-transform techniques then the various energy of faults has been calculated using Parseval’s theorem. Then these energies are given as an input to ANN. The classification results obtained show the effectiveness of this technique.100 percent classification is achieved by using this method

A normalized Haar wavelet transformation based firefly optimization algorithm for power transmission line fault detection problems

ABSTRACT Through transmission lines (TLs), an electric power transmission system has been able to transmit power from generating stations to consumers. During transmission, various kinds of malfunctions take place and they are termed as a fault. Although fault is undesirable, it is unavoidable event hampering the smooth functioning of the power system. In power transmission systems, and a large number of voltage and current signal, distortions take place due to faults. Faults occur in power TL causing power supply interruption. Several fault detection techniques have been presented by researchers to detect a fault in TL. However, the time required to locate the fault remained higher and the power loss rate (PLR) was not reduced. To overcome these issues and identify faults in electrical power TL, Haar wavelet feature extraction-based firefly optimized fault detection (HWFE-FFOFD) method has been introduced. The power TL signal sample has been taken as an input. Zero-mean normalization is the pre-processing approach that converts the transmission signal sample into a specified range. To extort features (i.e. voltages and current values) with higher accuracy, the normalized signal has been given to Haar Wavelet Transform. Then, the extracted features at different time instants have been given to the firefly optimized fault detection (FFOFD) algorithm. In the FFOFD algorithm, extracted features have been considered as firefly populations. The FFOFD algorithm functions with the flashing behavior of a firefly. At last, the firefly position has been updated and ranked according to light intensity to detect a fault in electrical power TL. In this manner, the fault detection time (FDT) gets reduced using HWFE-FFOFD method. HWFE-FFOFD method is evaluated in FEA, FDT, and PLR. From the experimental results obtained, it can be confirmed that the HWFE-FFOFD method has been able to enhance accuracy by 14% and minimize time by 26% and PLR by 62% when compared to conventional methods.

A novel line parameter adaptive phasor domain fault location method for double-circuit transmission lines

• Accurate phasor domain fault location for double-circuit transmission line. • Fault location without acquiring information of line parameters. • Uniqueness and convergence of the fault location equation solution. Phasor domain methods had been commonly used in power transmission line fault location. To improve the accuracy and line parameter adaptive ability of phasor domain fault location, this paper studies a two-terminal phasor domain line parameter adaptive fault location method. First, this paper analyses the convergence issue of original multi-dimensional non-linear fault location equations. Then, by regarding the four complexes consist of fault distance and line parameters as the new unknowns and thereby supplementing a constraint equation about the new unknowns, a quadratic fault location equation set is reconstructed. Furthermore, it is mathematically proven that the four new unknowns are all within the first quadrant. Therefore, the elements in the Jacobian matrix of the reconstructed equation set are all proven to be of symbolic invariance within the solution space. Thus, by using Newton-Raphson method and setting the initial iteration value of the four unknowns to any complexes within the first quadrant, the iteration solution of the reconstructed equation set will converge to the true solution. Finally, electro-magnetic fault simulations are used to evaluate the proposed method. The test results demonstrate that the proposed method is able to guarantee fault location accuracy under different circumstances without knowing line parameters.

Application of Wavelet Packet and Fuzzy Algorithm in Power System Short Circuit Fault Classification

Short-circuit fault, also known as a lateral fault, is one of the common types of faults in power transmission lines. This paper proposes an accurate method for identifying short-circuit fault types for power transmission line systems. According to the three-phase A, B, and C values, short-circuit faults can be subdivided into ten classes. An MDL power system with controllable short-circuit fault types is designed and tested. The method of using wavelet packet to analyze short-circuit fault waveforms and using the fuzzy controller to analyze eigenvectors is also proposed. Wavelet packet analysis makes up for the defect that wavelet transform usually only decomposes low frequencies and retains high frequency. Therefore, more accurate frequency band information and eigenvectors are obtained through wavelet packet transform, and then, the fuzzy controller is used to identify the eigenvectors, which can effectively detect and discriminate short-circuit fault types. Accurate identification of short-circuit fault types can improve transmission lines’ overall operational efficiency and promote the power system’s development.

A robust and accurate discrete Fourier transform‐based phasor estimation method for frequency domain fault location of power transmission lines

Frequency domain methods had been commonly used in power transmission line fault location. To improve the accuracy of frequency domain fault location methods, this paper studies a robust and accurate discrete Fourier transform (DFT)-based phasor estimation method. First, this paper analyses the deviation characteristics of DFT output phasors caused by decaying DC components with data window sliding. Based on these characteristics, this paper employs rotation factor computation to construct linear equations for the purpose of estimating the accurate fundamental frequency phasor during fault transient process. In this way, the accurate fundamental frequency phasor can be calculated by a two-step linear operation. Furthermore, to eliminate the effect of off-nominal frequency, the proposed method adjusts frequency deviation by using interpolation method. To further enhance the robustness for frequency domain fault location, the PauTa criterion is used to exclude the anomalous equations in the phasor estimation process. Finally, electro-magnetic fault simulation is used to evaluate the proposed method. The test results demonstrate that the proposed method improve the accuracy of fault location during the fault transient process.

Impact of Resistive SFCLs on Superimposed Quantities in Power System Faults

Superimposed quantities, also known as delta quantities, are the transient signatures of fault currents and voltages that can be utilized by protective relays to achieve fast fault detection for electric power grid applications. With the installation of superconducting fault current limiters (SFCLs) on power transmission lines, the profiles of superimposed quantities in power system faults are affected due to the altered impedance properties at transmission line terminals, which may further affect protective relay performance. In this article, we investigate the impact of a resistive SFCL on superimposed quantity profiles. The equivalent circuit of a resistive SFCL connected to an electric power transmission system is first illustrated based on which the simulation model is established in an electromagnetic transients program. Various types of power transmission line faults are simulated. The superimposed current and voltage quantities at transmission line terminals are obtained. The simulation results demonstrate that the acquired superimposed quantities are significantly affected by the SFCL. Possible consequences to protection functions are discussed.

Research on Adaptive Power Transmission Line Fault Inspection

Power transmission line is one of the most important infrastructures of power system, and its safety monitoring is of great significance. The conventional way of fault monitoring of power transmission lines by only setting threshold value on single temperature data of strain clamp turned out to multiple misjudgements and delayed alarms, causing the increment of operation risk of power transmission line. In this paper, various types of time-varying sensors data such as strain clamp temperature data, environmental data, and cable ampacity data are accounted. Also, an unsupervised machine learning algorithm - K-means clustering algorithm was introduced to build a discriminant model in detecting the defects of power transmission line. Experimental results proved that the proposed method is able to avoid delayed alarms as well as misjudgement incurred from conventional method. As a result, the operation safety of power transmission line and inspection efficiency can be improved. The inspection cost would be reduced as well.

Power Transmission Line Fault Detection and Diagnosis Based on Artificial Intelligence Approach and its Development in UAV: A Review

This paper provides a systematic, comprehensive, up-to-date study of the various artificial intelligence (AI) techniques on the detection and classification of faults on power transmission line. We review the latest start of the art of various intelligent approaches and even discuss the differences and outcomes of implementing intelligent methods in the protection scheme and the integrated protection scheme. Besides, there has been an increase in demand and interest for the application of AI approaches in drone to aid in detection and classification of faults. However, there are not many surveys pertaining to the development of the unmanned aerial vehicle (UAV) for the application of intelligent methods in this field. Thus, we also include in this paper the literature relevant to the implementation of various intelligent approaches in the unmanned aerial vehicle (UAV) for the fault detection and classification on power transmission line. Finally, we discuss the challenges and limitations faced in the implementation and propose ways to bridge the gap in the field for further research. This comprehensive study can act as a platform for new researchers to assess the possible different intelligent methods in detecting and classifying faults on power transmission line with a set of references that were dedicated to the attentive contributions.

A novel Fault Diagnosis Method for Power Transmission Lines Based on Improved Deep Belief Network

With the rapid development of artificial intelligence technology, the application of deep neural networks to power transmission line fault classification is gaining more and more attention from researchers. In this paper, a novel power transmission line fault classification method based on an improved Deep Belief Network is proposed. The Adam algorithm is used to adjust the learning rate. The convergence rate can be faster and the probability of falling into a local extremum is reduced. This paper uses IEEE 30-bus system as an example for simulation experiment. The electrical parameters and their zero components at the key buses of the power system are selected as eigenvalues. Experimental results show that, contrary to conventional Deep Belief Network method, the improved method has higher classification accuracy and faster convergence. When we increase the noise level of the dataset, the improved Deep Belief Network still has better classification effect.

Vitality Burglary Recognition and Issue Investigation Dependent on IOT

The proposed idea on this paper pursuits on the format and usage rendition of electrical vitality theft recognition and fault assessment. The unlawful use of intensity must be fathomed through advanced way, with no human cooperation. The reason for this work is to give a usage approach to control burglary discovery and controlling which licenses violators to be recognized at a faraway region. This design integrates various approach for problems faced by way of energy distribution device inclusive of power theft and transmission line fault. It consists of IOT era to discover the electrical theft and transmission line fault. In the proposed approach we offer alert to the authorized electricity issuer via an alert message which eliminates the diverse problems associated with the meter studying and theft detection and it blocks the energy deliver to the specific vicinity.

An edge computing method using a novel mode component for power transmission line fault diagnosis in distribution network

The commonly used fault diagnosis method of the power transmission line (PTL) is the traveling wave method which uses the traditional mode components $$\alpha $$ , $$\beta $$ and o to realize the fault type recognizing and the fault location in distribution network. However, the traditional mode components of the traveling wave method may take the value of zero that cannot provide the effective fault feature for the A-phase ground fault, or the A and B double phases short-circuit fault. Moreover, a single traditional mode component cannot recognize all the types of the faults, and the fault recognition has to use the multiple mode components that increases the complexity. To address the aforementioned issues, this paper proposes an edge computing scheme based on a novel mode component $$\gamma $$ for PTL fault diagnosis in distribution network. Firstly, the novel mode component $$\gamma $$ is fused according to the traditional mode components $$\alpha $$ and $$\beta $$ , then the propagation property of the traveling wave- $$\gamma $$ is derived. Secondly, the edge computing scheme and the PTL fault diagnosis method are designed for distribution network by using the only novel mode component $$\gamma $$ . Thirdly, the relationship table of the fault type and the boundary conditions as well as the component $$\gamma $$ expressions is derived for the fault recognition using the only traveling wave- $$\gamma $$ . In addition, the fault distance computing is explored by using the traveling wave- $$\gamma $$ . The computing is composed with the wave- $$\gamma $$ head identification using the optimal Wavelet vanishing moment parameter, and the arrival time determination, and the fault location method. To evaluate the efficiency of the proposed edge computing scheme, the simulation experiment and the laboratory tests are conducted. The simulation experiment results show that, the novel mode component $$\gamma $$ not only provides the effective fault features for all the ten types of the power transmission line faults, but also increases fault locating accuracy by the average 2.08 % compared with the methods using the traditional mode component $$\alpha $$ , $$\beta $$ and o. Meanwhile, the laboratory tests show that the edge computing method is practical.

Improving the stability of power transmission lines based on an adaptive single pole auto‐reclosure using a two‐step strategy

As more than 80% of single line to ground faults in power transmission lines (PTLs) are transient, single pole auto-reclosure (SPAR) is of considerable importance for increasing the successful SPAR rate, improving power system stability and reducing shocks to the power systems. In this study, a new two-step strategy for ASPAR is presented. In the first step, the fault type (permanent or transient) is determined using the derivative of the first harmonic of the faulty phase voltage. Then in the second step, the extinguishing moment of the secondary arc is detected in the case of transient occurrence, in which the reclosing signal is sent to the side circuit breakers of the PTL after the secondary arc is completely extinguished. The major features of the proposed scheme are: its capability to detect the permanent fault from the transient one, and determine the extinguishing moment of the secondary arc without the need to define a specific threshold level, its independence from the PTL type and its appropriate structure that facilitates its online hardware implementation and makes it more comprehensive. Extensive simulations in the ElectroMagnetic Transient Program-RV, as well as practical data testing, demonstrate the accuracy and precision of the proposed method.

Review on solar disturbance studies and electric power transmission line fault in Malaysia

<span lang="EN-GB">This work is a review on previous research and studies on the solar disturbance activities and faults that occur on the electric power transmission line. Research has shown that low latitude regions could also be affected by solar disturbance events. The effects of these events on earth are considerable and may cause electric power transmission failure. Geomagnetically induced current is one of the ground impacts to the transmission line, which may cause fault or disturbance and consequently power system failure. Solar disturbance data from magnetometers and electric power transmission line fault data from a digital fault recorder are reviewed in this work. These data would be valuable in determining the characteristics and effects of solar disturbances on Malaysia’s electric power transmission lines.</span>

Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data.

The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Transmission Line Fault Classification Using Hidden Markov Models

The maintenance of power quality in electrical power systems depends on addressing the major disturbances that may arise during generation, transmission and distribution. Many studies aim to investigate these disturbances by analyzing the behavior of the electrical signal through the classification of short circuit faults in power transmission lines as a way to assist the administration and maintenance of the electrical system. However, most fault classification methods generate a high computational cost that do not always yield satisfactory results; these methods utilize front ends in data processing before being processed by conventional classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) that are adopted into the Frame Based Sequence Classification (FBSC) architecture that uses the front ends Wavelet energy, Waveletconcat, RAW, Root Mean Square front ends (RMS) and Con cat Front End. An alternative method for classifying faults without having to use front ends employs the UFPAFaults database and the Hidden Markov Model (HMM) algorithm that directly treats the electrical signal in the form of multivariate time series. The results indicate the HMM algorithm as a potential classifier because its comparatively low error rate of 0.03% exceeds the performance of the conventional classifiers ANN, SVM, KNN and RF as used with the FBSC architecture. When the statistical test with a significance of α =5 % is applied, only the ANN and RF classifiers present a result close to what the HMM algorithm provides. Another relevant factor is that the HMM algorithm considerably decreases the computational cost by more than 90% of processing time as compared to the conventional classifiers of the FBSC architecture, thereby validating its potential in the direct classification of faults in electric power system transmission lines.