Transmission Line Fault Research Articles

Method for lightning flashover types identification on transmission lines utilizing OPGW optical polarization state signal characteristics

The Optical Fiber Composite Overhead Ground Wire (OPGW) has undergone rapid development and has been extensively applied in the realm of intelligent power system status monitoring. This article conducts research on lightning signal waveform in optical fibers within the context of lightning fault location using the OPGW optical polarization state method. A transmission line model is simulated employing ATP-EMTP to capture electrical characteristics when lightning strikes the OPGW. Subsequently, by employing the Faraday magneto-optical effect principle, electrical signals are transformed into optical polarization states within the internal fibers of OPGW. Using such data, the optical signal propagation characteristics when lightning strikes OPGW are deduced. Finally, a method for discerning between induced lightning and direct lightning strikes through the analysis of time-frequency domain characteristics in OPGW. This method addresses the limitation of traditional OPGW lightning monitoring, which is unable to deduce electrical signals, thus achieving precise identification of lightning-induced transient faults in transmission lines.

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection.

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

Detection and identification of fault in transmission lines based on ANN

Detection and identification of fault in transmission lines based on ANN

Analyses of unexplained faults in transmission lines in the power grid of Ethiopia

Ethiopian power transmission lines operating at 132 kV, 230 kV and 400 kV are encountering unacceptably high number of unexplained outages caused by external factors, which compromise the reliability of the system. It is therefore crucial to examine the characteristics and underlying causes of these outages through analyses of long-term records of the faults for identifying their probable causes for best targeting future mitigation measures.A method is presented to identify, in retrospect, the major possible root causes of unexplained outages that combines information on fault time-of-the day and time-of-the year records with flashover characteristic of known fault causes and local climactic and environmental information. The analyses are based on data covering an 8-year period from 2015 to 2022. A total of 8891 fault records were compiled and among them 4231 faults (47 %) were unexplained faults. Results show statistical variations in the frequency of unexplained faults with respect to spaciotemporal occurrence of the faults, voltage level and local climate.Moreover, the analyses indicate that out of the total number of 4231 unexplained faults, 787 faults could be attributed to lightning (19 %), 1135 faults to pollution events (27 %), 479 were fire related (11 %) and 1830 faults caused by external interferences (43 %).

A pilot protection based on low frequency component model for HVDC transmission lines

AbstractReliable and fast identification of high‐voltage direct current (HVDC) transmission line faults is important to the safety of power system. In traditional DC transmission line protection, differential protection is invulnerable to high fault resistance, but it needs long time delay for the influence of distributed capacitance current. In order to solve this problem, a novel pilot protection is proposed. The characteristics of the voltage and current processed by low‐pass filter under different fault conditions are analysed, and based on it the protection criteria is proposed. When external fault occurs, it reflects the line capacitance where the first forward fault traveling wave passes, which is increasing until equal to the whole line capacitance, then remains constant. However, when internal fault occurs, its value is negative in the initial stage, and after that its value is very different from the whole line capacitance. The analysis proves that this pilot protection method has fast operation speed, and can be applied for a relatively long period of time. What is more, the applicable time of superposition principle is considered in the analysis, which is not considered in many literatures. An UHVDC system is modelled for simulation in PSCAD/EMTDC, and the correctness and effectiveness of the proposed scheme is verified.

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.

Analysis of Fault in Transmission Line by Using MATLAB/Simulink

Analysis of Fault in Transmission Line by Using MATLAB/Simulink

New energy transmission line fault location method based on Pearson correlation coefficient

The access of new energy power source makes the traditional transmission line structure become complex, and due to the influence of power electronic device control strategy, the fault characteristics have been fundamentally changed, resulting in the traditional transmission line fault localization method can not be applied to the new energy sending line. To address the above problems, this paper analyzes the transient current characteristics of different power supply faults, and learns that there are obvious differences in the transient currents on both sides of the fault point inside and outside the transmission line area, and then proposes a new energy transmission line fault localization method based on the Pearson correlation coefficient, which determines the fault location by calculating the correlation coefficients of the fault waveforms of the neighboring monitoring points. Finally, comparative experiments are carried out under different fault types and fault locations to further verify the applicability of the proposed method.

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.

Effect of line length on the fault current due to line to ground fault in transmission line using MATLAB Simulink

Today, electrical power is a necessity for many aspects of daily living, including works in homes, businesses, industries, and agriculture. This article is created to ensure that the electrical power needed by these sectors is maintained. In an electrical system, numerous faults can arise owing to line-to-ground (L-G), line-to-line (L-L), and three lines (L-L-L). This study addresses the analysis of fault current under line-to-ground (L-G) and how it varies with the fault position. MATLAB Simulink has been utilized in this work to demonstrate how fault current depends on the line length of transmission system during L-G faults.

A pilot protection scheme based on high‐frequency transient current waveform similarity for AC lines connected to HVDC inverter station

AbstractAffected by inverter station commutation failure and fast control, line fault characteristics are obviously different from traditional AC transmission line fault characteristics. The existing protection methods for AC line connected to line commutated converter based high voltage DC (LCC‐HVDC) inverter station (ACLs) have the problems of complex setting, weak anti‐noise and high demand for data synchronization. This paper proposes a pilot protection scheme based on high‐frequency transient current waveform similarity. Firstly, with analyzing the fault traveling wave propagation process, the obvious differences are found in the transient current waveforms at both ends of the line when internal and external faults occur. Then the similarity of transient current waveforms is measured with the improved longest common similar subsequence (LCSS) method. And the weak high‐frequency current is extracted with the improved synchronous squeezed S‐transform (SST) method. Finally, a large number of power systems computer aided design (PSCAD) simulation results show that the proposed protection method does not require strict data synchronization, has strong anti‐noise performance, and is tolerant to the influence of abnormal point fluctuations.

Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI).

A large volume of data is being captured through the Phasor Measurement Unit (PMU), which opens new opportunities and challenges to the study of transmission line faults. To be specific, the Phasor Measurement Unit (PMU) data represents many different states of the power networks. The states of the PMU device help to identify different types of transmission line faults. For a precise understanding of transmission line faults, only the parameters that contain voltage and current magnitude estimations are not sufficient. This requirement has been addressed by generating data with more parameters such as frequencies and phase angles utilizing the Phasor Measurement Unit (PMU) for data acquisition. The data has been generated through the simulation of a transmission line model on ePMU DSA tools and Matlab Simulink. Different machine learning models have been trained with the generated synthetic data to classify transmission line fault cases. The individual models including Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (K-NN) have outperformed other models in fault classification which have acquired a cross-validation accuracy of 99.84%, 99.83%, and 99.76% respectively across 10 folds. Soft voting has been used to combine the performance of these best-performing models. Accordingly, the constructed ensemble model has acquired a cross-validation accuracy of 99.88% across 10 folds. The performance of the combined models in the ensemble learning process has been analyzed through explainable AI (XAI) which increases the interpretability of the input parameters in terms of making predictions. Consequently, the developed model has been evaluated with several performance matrices, such as precision, recall, and f1 score, and also tested on the IEEE 14 bus system. To sum up, this article has demonstrated the classification of six scenarios including no fault and fault cases from transmission lines with a significant number of training parameters and also interpreted the effect of each parameter to make predictions of different fault cases with great success.

Resolution and Analysis of Transmission Line Fault Types Based on Recording Type Data and Deep Learning

The reliable identification of fault types in transmission lines is essential for restoring power supply swiftly and minimizing economic losses during outages, thereby ensuring the safe and efficient functioning of the power system. This paper addresses the challenge of low recognition accuracy in existing transmission line fault diagnosis methods and presents a novel approach based on fault recording data collected from both ends of the line. This method distinguishes between lightning-strike and non-lightning-strike faults, utilizing a deep learning network architecture to analyze time-domain information from recorded data, using the initial and terminal waveforms as inputs. The proposed fault identification model integrates fault current phase mode transformation, Local Mean Decomposition (LMD) decomposition, and spectral entropy analysis, applying deep learning principles to enhance fault detection precision. This comprehensive approach enables the effective identification of various fault types on transmission lines. Extensive simulation tests were conducted using a sophisticated fault simulation model developed within simulation software to validate the proposed algorithm’s efficacy. The results demonstrate the algorithm’s high accuracy and efficiency in recognizing various fault types on transmission lines.

A Fault Location Algorithm for Multi-Section Combined Transmission Lines Considering Unsynchronized Sampling

This paper introduces a fault location technique for multi-section combined transmission lines, utilizing unsynchronized fault record data. The approach determines the intersection points of the voltage magnitudes computed using the fault current and voltage records of each terminal and the constant line parameters of each section. Finally, the real fault location is identified based on the calculated inverse angle of equivalent impedance. To evaluate the performance of the proposed algorithm, a three-section combined transmission line is constructed using the PSCAD/EMTDC software version 5.0.0, and various fault scenarios are simulated and tested employing the proposed algorithm. The results indicate that the proposed algorithm is applicable to various types of faults in a multi-section combined transmission line; the estimations are accurate.

Transmission line fault detection based on anchor frame a priori and attention mechanism

YOLO target detection algorithm is the mainstream method for image-based defect detection of transmission line insulators. However, the existing model complexity is large. A reasonable and effective parameter compression method is urgently needed as a prerequisite to lay the foundation for solving the dilemma of UAV edge equipment deployment. At the same time, the insulator defect images taken by UAVs have complex backgrounds and small defect sizes, which are prone to misdetection and omission. It is needed to improve the channel domain of the CBAM (convolution block attention module) attention mechanism and solve its problem of missing channel information due to dimensionality reduction. The improved CBAM is added to the backbone network of YOLOv5s. The goal is to enable the model to identify and pinpoint the critical objectives with greater precision. The model is combined with Cross Entropy Loss and Lovasz-Softmax Loss weighting, which makes the network converge more stably during the training process, and the accuracy rate is also somewhat improved.

Lightweight Transmission Line Fault Detection Method Based on Leaner YOLOv7-Tiny.

Aiming to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones, this study proposes a novel lightweight model called Leaner YOLOv7-Tiny. The primary goal is to swiftly and accurately detect typical faults in transmission lines from aerial images. This algorithm inherits the ELAN structure from YOLOv7-Tiny network and replaces its backbone with depthwise separable convolutions to reduce model parameters. By integrating the SP attention mechanism, it fuses multi-scale information, capturing features across various scales to enhance small target recognition. Finally, an improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results demonstrate a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection.

Intelligent Processing of Power Operation Data Based on Improved Apriori Algorithm

This paper addresses several problems in the power system. Key challenges include low-power information integration, inappropriate system data management, inaccurate system data updating, and inefficient fault diagnosis. We focus on analyzing and diagnosing transmission line faults using the operation data of the power system. The study incorporates the quantitative identification of statements. This is done using the Apriori big data analysis and calculation method. Additionally, we utilize big data analysis and vast power operation data. We aim to achieve automatic analysis and pinpoint the causes of transmission line faults. Furthermore, we seek to optimize the traditional Apriori calculation method. This optimization results in a reduction of about 52% in the candidate item set calculation. The optimized M-Apriori calculation method can analyze the correlation between event index data and faults in real time, and realize automatic diagnosis and analysis of faults through operation data.

A fault recognition method for transmission systems based on independent component analysis and convolutional neural networks

Accurate recognition of transmission line (TL) faults plays a vital role in protection systems, avoiding misoperations and false trips. Several studies in this area have revealed the limitations of existing algorithms, particularly when considering more complex electrical power systems (EPS), where some expected operations (capacitor and load switching, for example) can be misinterpreted as fault events. This work addresses the critical issue of fault recognition (FR) in TLs with a special focus on distinguishing faults from other types of disturbances. The primary objective of this study is to introduce an approach based on Convolutional Neural Networks (CNN) and Continuous Wavelet Transform (CWT) for FR in EPS. This method relies only on voltage signals acquired with a reasonable sampling frequency. The proposed method is enhanced with an Independent Component Analysis (ICA)-based preprocessing, making it robust and versatile in handling different types of disturbances, such as adjacent line faults, load switching and capacitor switching. The evaluation of the proposed method is done considering two different EPS, achieving an accuracy up to 99 %, showing the robustness of the method against different scenarios such as fault events and switching operations.

VSC-HVDC transmission line fault location based on transient characteristics

VSC-HVDC transmission line fault location based on transient characteristics

System for Locating Faults in Multiterminal Transmission Lines

System for Locating Faults in Multiterminal Transmission Lines