Fault Line Selection Research Articles

Fault-Line Selection Method in Active Distribution Networks Based on Improved Multivariate Variational Mode Decomposition and Lightweight YOLOv10 Network

Fault-Line Selection Method in Active Distribution Networks Based on Improved Multivariate Variational Mode Decomposition and Lightweight YOLOv10 Network

Fault Line Selection of Small Current Grounding System Research Based on Zero Sequence Circuit Current Phase

Abstract This paper investigates fault line selection in small current grounding systems, focusing on the zero sequence current phase characteristics. Small current grounding systems, prevalent in medium voltage distribution networks, face challenges in fault detection due to minimal fault currents during single status phase grounding faults. Traditional methods relying on manual line switching and zero sequence voltage monitoring are inefficient and can induce overvoltage. This study categorizes fault line selection techniques into three approaches: steady-state fault information, transient information, and modern signal processing technology. The analysis of zero sequence currents in non-grounded systems reveals that fault line zero sequence currents equal the sum of system capacitive currents, flowing opposite to those in non-fault lines. Additionally, transient signal analysis shows that these signals provide more robust information for fault detection. MATLAB simulations validate the theoretical models, demonstrating that zero sequence current direction and amplitude effectively identify fault lines. The results confirm the efficacy of combining steady-state and transient signal analyses for accurate fault line selection, contributing to enhanced safety and reliability in power distribution networks. This research offers practical insights and tools for engineers, improving fault management and reducing potential economic losses in small current grounding systems.

Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer

Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer

Correction: Integrated dynamic spiking neural P systems for fault line selection in distribution network

Correction: Integrated dynamic spiking neural P systems for fault line selection in distribution network

Integrated dynamic spiking neural P systems for fault line selection in distribution network

Integrated dynamic spiking neural P systems for fault line selection in distribution network

Fault line selection algorithm for single-phase high-impedance ground faults in distribution networks based on the hilbert-huang transform

Fault line selection algorithm for single-phase high-impedance ground faults in distribution networks based on the hilbert-huang transform

Fault line selection algorithm for single-phase grounding faults in distribution networks based on multi-feature fusion of multi-level wavelet decomposition coefficients

Fault line selection algorithm for single-phase grounding faults in distribution networks based on multi-feature fusion of multi-level wavelet decomposition coefficients

A multi-feature fusion algorithm for single-phase high-impedance ground fault line selection in distribution networks based on Hilbert-Huang transform

A multi-feature fusion algorithm for single-phase high-impedance ground fault line selection in distribution networks based on Hilbert-Huang transform

Research on fault line selection method based on steady state variables

When a single-phase ground fault occurs, the transient zero sequence current will significantly increase. So, it is possible to detect whether a single-phase grounding fault has occurred in the transmission line by calculating and comparing the amplitude of transient zero sequence current. Only when the amplitude of the zero sequence voltage under the steady-state power frequency is greater than the preset threshold can the line selection device be effectively and reliably started. The instantaneous value protection device can also be used to start first, and then, the steady-state amplitude of the power frequency can be compared as the verification object.

Fault Line Selection Method for Power Distribution Network Based on Graph Transformation and ResNet50 Model

Fault Line Selection Method for Power Distribution Network Based on Graph Transformation and ResNet50 Model

Line Selection of Single-Phase Fault Grounding System Based on the Deviation Criterion of Composite Zero-Sequence Admittance

Abstract The current methods for line selection of small current fault grounding system faults within distribution networks have some limitations. This paper proposes a method based on the deviation criterion of composite zero-sequence admittance for line selection of single-phase fault grounding systems with small currents. First, the derivation formula of zero-sequence admittance for small current grounding systems considering asymmetrical transmission lines is presented. Then, a method based on the deviation criterion of composite zero-sequence admittance is proposed for accurate fault line selection. Finally, a fault model is built by using simulation software and simulation experiments are conducted to verify that this method can rightly select the fault line by significant feature values.

SSE-YOLOv5: a real-time fault line selection method based on lightweight modules and attention models

SSE-YOLOv5: a real-time fault line selection method based on lightweight modules and attention models

Enhanced fault localization in multi-terminal transmission lines using novel machine learning

Accurate fault location on transmission lines is paramount for ensuring the reliable and efficient operation of the electricity grid, which underpins every aspect of modern society. Existing fault localization methods for transmission lines often face shortcomings, particularly in scenarios involving multi-terminal transmission lines, where complexities arise due to dispersed generations and intricate network configurations. Traditional approaches may struggle to provide accurate fault localization, impacting the reliability and efficiency of the electricity grid. Research provide a unique fault localization technique in this article depends on Phasor Measurement Units (PMUs) and Bidirectional Gradient Boost Random Forest (BDGB-RF) machine learning technique to address these challenges. The proposed method offers several advantages over traditional methods, including enhanced accuracy and efficiency. By leveraging PMU data and BDGB-RF, the method provides a two-phase fault localization strategy, incorporating fault line selection based on nodal current imbalance and subsequent fault distance determination. Simulation results demonstrate the effectiveness of the proposed approach, achieving up to 97% accuracy in the majority of studied scenarios, even under different tapping configurations. The adoption of this approach could significantly impact the practices of experts in the field, facilitating more reliable fault detection and localization in complex transmission line networks. This, in turn, can contribute to the resilience and stability of power systems, ultimately improving grid reliability and minimizing downtime.

Faulty feeder selection based on improved Hough transform in resonant grounded distribution networks

Due to the weak fault characteristics and inaccurate fault line selection during high impedance faults, this paper proposes a new method for fault line selection in resonant grounded distribution networks based on improved Hough transform. Firstly, the variational modal decomposition (VMD) algorithm decomposes each feeder's zero sequence power under different operating conditions, then, using Sample entropy and Pearson correlation coefficient, selects the intrinsic mode function (IMF) with the highest correlation strength with the zero-sequence power of each feeder, called IMFK. Secondly, using the Hough transform to detect IMFK, obtaining the fitted straight line and IMFK angle during the fault's initial stage. The problem of identifying the zero-sequence power mode trend is transformed into a mathematical problem of determining the geometric shapes of straight lines and curves in the zero-sequence power mode image. Finally, a line selection criterion is established by integrating correlation coefficients to identify the faulty feeder in the distribution network system when a single-phase grounding fault occurs. The PSCAD simulation and on-site measurement results show that the proposed method can ensure the effectiveness and adaptability of the line selection.

Research on Single-phase Grounding Fault Line Selection Based on VMD Method

Single-phase ground faults occur most frequently in low-current grounding systems due to the complexity of the power grid. For the stable. Analyzing the fault transient signal is necessary for detecting single-phase grounding faults, accurately extracting the characteristic frequency that reflects the fault information, and using the VMD method to model and simulate the fault information. The experiment involved studying simulation data of single-phase grounding short circuits in two types of grounding distribution network models, namely overhead line and cable hybrid line, under different fault conditions. By utilizing the intrinsic modal energy, the fault line was determined, which enabled quick selection of the fault line. The effectiveness of this approach was confirmed through verification.

A novel distributing–collecting on-line insulation monitoring system and line selection technology for the DC supply system

At present, the power supply system of 5G base stations is a micro smart grid, it generally uses 240 V DC power supply with multiple branches, and leakage accidents will threaten personal and property safety, so it is vital to identify the fault line accurately and remove the faults rapidly. In this paper, the leakage phenomenon of transmission lines in the HVDC power supply system of a 5G communication base station is studied. To address the issue of multi-branch line leakage diagnosis and line selection in the 240 V DC system, a new distributed DC insulation monitoring and fault line selection technology and system are proposed. The distributed DC insulation monitoring line selection technology is used to collect the leakage current of each branch by setting a unique single-core DC leakage transformer and process the signal primarily. In addition, a high-resistance bridge switchgear is added to the bus bar. According to different leakage currents caused by the imbalance of the bridge before and after the switch, the insulation resistance of electrodes to the ground is calculated accurately, which solves the problem that the current technology cannot judge the fault of the positive and negative electrodes to the ground at the same time. Through both simulation and prototype experiments, the feasibility of this technology and system device in line insulation monitoring and line selection of the HVDC communication base station power supply system is verified.

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

AbstractWhen a grounding fault occurs in the distribution network with distributed generation, the network topology becomes intricate, making it challenging to extract fault characteristics, resulting in a decrease in the precision of fault discrimination. To address this issue, a graph isomorphism network (GIN) approach based on the parameterized adaptive graph learning (AdapGL) module is proposed, transforming the distribution network fault selection problem into a graph classification task. First, the adjacency matrix of the distribution network's topology graph is initialized. This matrix, combined with feature vectors from the robust local mean decomposition energy entropy and transient dielectric loss angle of line, will be input into the GIN. Then, the AdapGL module is integrated into the GIN, dynamically learning and updating the one‐way relationships between actual network nodes to complete the graph classification task. Finally, a radial distribution network model (RDNM) and an improved IEEE 34 nodes model are established, and the fault selection results of the AdapGL‐GIN method are compared with those of other methods. The results indicate that the proposed method achieves higher accuracy than other methods, demonstrating significant practical importance in engineering applications.

High-Resistance Grounding Fault Detection and Line Selection in Resonant Grounding Distribution Network

The detection and selection of fault lines in resonant grounding distribution networks pose challenges due to the lack of sufficient state parameters and data. This paper proposes an approach to overcome these limitations by reconstructing the initial criterion for fault occurrence and fault line selection. Firstly, a combination of 15% of the traditional phase voltage and the sum of the zero-sequence voltage gradient is suggested as the initial criterion for fault occurrence. This improves the speed of the line selection device. Additionally, the transient process of high-resistance grounding in a resonant grounding system is analyzed based on the impedance characteristics of high- and low-frequency lines. The line selection criterion is then established by comparing the current and voltage derivative waveforms on high- and low-frequency lines. To verify the effectiveness of the proposed method, simulations are conducted. The results demonstrate that this method can effectively handle high-resistance grounding faults under complex conditions while meeting the required speed for line selection.

Correlation and K-means clustering based small current ground fault line selection algorithm

The existing small current grounding line selecting technique has consequences by transition resistance, fault closure angles, and the fault location, and there is a problem of low selection accuracy, which is addressed by proposing a line-choosing algorithm that employs a combination of transient zero-sequential current (TZSC) waveform correlation and K-means clustering. First, to obtain the correlation coefficient matrix, the correlation analysis is performed for each line of the zero-sequence currents transient measurement values; second, to address the issue of the difficulties in manually setting the threshold, we apply the K-mean cluster analysis algorithm to group the row vectors of the correlation coefficients matrices; and finally, we output the label of the cluster to realize the fault line selection. Simulation and real-world fault data validate the method, showing that it is independent of transition resistance, fault closure angles, and fault location. It can achieve precise and dependable line selectivity.

A single-phase ground fault intelligent wire selection method based on an ant colony algorithm

For the single-phase ground fault, which has the highest probability of occurrence in a 10 kV distribution network, the ant colony algorithm is combined with a neural network for fault line selection, and the algorithm is optimized. The study builds a 10 kV distribution network model in MATLAB and simulates the neutral ground fault. It compares the results of the optimized line selection algorithm, and the traditional line selection algorithm proves that the accuracy of the method in identifying faulty lines is significantly better than that of the traditional method.