Ground Fault Line Research Articles

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

Formulation of Fault-Branch Currents During Cross-Country Faults on Asymmetrical Double-Circuit Transmission Lines Considering Status of Line-Ground Faults’ locations

Formulation of Fault-Branch Currents During Cross-Country Faults on Asymmetrical Double-Circuit Transmission Lines Considering Status of Line-Ground Faults’ locations

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.

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.

Optimize single line to ground fault detection in distribution grid power system using artificial bee colony

The most common power system (PS) distribution network fault, single lineto-ground fault (SLGF), causes residual current (I res) to start an electrical arc and high voltage (HV) three times the rated voltage in other healthy phases. HV from capacitive currents (IC) damages cable insulation and PS appliances. Peterson The neutral point coil (PC) reduces (I res) and extinguishes the electric arc, but the fault current (I fault) remains below the protection devices' threshold. Operations and equipment are riskier. PC adaptive eliminates electrical arcs, making the network safer. This paper detects I faults online using Texas instrument validation in MATLAB and adaptive by artificial bee colony (ABC). This paper discusses Texas instrument fault current detection and MATLAB validation. It improves system reliability, device protection, and copper savings by thousands of tons. ABC intelligently optimizes many mathematical problems. ABC with network neural artificial intelligence (AI) improves algorithm performance (artificial bee colony network neural (ABCNN)). This new method may improve distribution network SLGF detection. This first work can work online in electrical power stations by building the (eZdsp F28335-RS232) into the program to send fault signals to the control when SLGF occurs without damaging devices, equipment, cables, or power outages.

Prevention of Wildfires Using an AI-Based Open Conductor Fault Detection Method on Overhead Line

Overhead lines that are exposed to the outdoors are susceptible to faults such as open conductors on weak points and disconnection caused by external factors such as typhoons. Arcs that occur during disconnection generate energy at a high heat of over 10,000 °C, requiring swift fault shut-off. However, most conventional fault detection methods to protect electrical power systems detect an overcurrent; thus, they can only detect faults after the line is disconnected and the cross-section of the line that generates the arc discharge makes contact with another line or the ground, causing a high risk of fire. Furthermore, in the case of ground faults owing to the disconnection of overhead lines, the load and the grounding impedance are not parallel. Therefore, in the case of the fault current not exceeding the threshold or a high impedance fault due to the high grounding impedance of the surrounding environment, such as grass or trees, it is difficult to determine overhead line faults with conventional fault detection methods. To solve these issues, this paper proposes an AI-based open conductor fault detection method on overhead lines that can clear the fault before the falling open conductor line comes into contact with the ground’s surface so as to prevent fire. The falling time according to the height and span of the overhead line was calculated using a falling conductor model for the overhead line, to which the pendulum motion was applied. The optimal input data cycle that enables fault detection before a line–ground fault occurs was derived. For artificial intelligence learning to prevent wildfires, the voltage and current signals were collected through a total of 432 fault simulations and were wavelet-transformed with a deep neural network to verify the method. The proposed total scheme was simulated and verified with MATLAB.

Artificial intelligence techniques for ground fault line selection in power systems: State-of-the-art and research challenges.

In modern power systems, efficient ground fault line selection is crucial for maintaining stability and reliability within distribution networks, especially given the increasing demand for energy and integration of renewable energy sources. This systematic review aims to examine various artificial intelligence (AI) techniques employed in ground fault line selection, encompassing artificial neural networks, support vector machines, decision trees, fuzzy logic, genetic algorithms, and other emerging methods. This review separately discusses the application, strengths, limitations, and successful case studies of each technique, providing valuable insights for researchers and professionals in the field. Furthermore, this review investigates challenges faced by current AI approaches, such as data collection, algorithm performance, and real-time requirements. Lastly, the review highlights future trends and potential avenues for further research in the field, focusing on the promising potential of deep learning, big data analytics, and edge computing to further improve ground fault line selection in distribution networks, ultimately enhancing their overall efficiency, resilience, and adaptability to evolving demands.

Discrimination method of low-current grounding fault of primary and secondary integrated equipment under three-phase asymmetric harmonic power flow calculation

In order to realize the accurate judgment of the ground fault and improve the fault discrimination effect, this paper proposes a low-current ground fault discrimination method for the primary and secondary fusion complete sets of equipment under the calculation of three-phase asymmetric harmonic power flow. The three-phase asymmetric harmonic power flow calculation is carried out, the ground fault line selection model is constructed according to the calculation results, and the faulted line is obtained by the zero-sequence active component method and the zero-sequence reactive power component method; the wavelet packet transform method is used to extract the transient zero-sequence power direction, and use it as a line selection criterion to identify whether a ground fault occurs. The amplitude characteristic enhancement value of each section is obtained by calculation. According to the distribution characteristics of the zero-sequence current amplitude of the faulted feeder, the corresponding section is selected as the fault section, and the mutation logic array is used in the determined fault section to realize the low-current grounding fault judgment. The experimental results show that the method has high judgment accuracy in practical application, and the highest value is 98.5 %, which indicates that the method can accurately judge the fault line and determine whether ground fault occurs.

One-Phase Ground Fault Line Selection Technology for Distribution Network Based on Comprehensive Analysis of Phase Current

Due to the various types and different characteristics of one line-to-ground faults occurring in the partial network which is responsible for distribution, the adaptability of location technology applicable to a one fault type is poor. To ensure the operational reliability of the partial network which is responsible for distribution, this thesis shows a one-phase ground fault line selection technology for distribution networks that is applicable to various types of faults. By using this technology, we can comprehensively analyze the characteristics of phase current in transient state and steady state when different types of faults occur, and fault line selection algorithms suitable for intermittent arcing and resistance grounding respectively were proposed according to the steady-state phase and transient mutation characteristics of phase current. In addition, the feasible region of the proposed line selection technology was expanded by using the comprehensive analysis algorithm combined with two-line selection algorithms, which ensures the reliability of selecting fault line after different types of one line-to-ground fault s occur in the system. Finally, the ATP simulation software was used to verify the reliability and engineering applicability of this technology.

Single-Phase to Ground Fault Line Identification for Medium Voltage Islanded Microgrids With Neutral Ineffectively Grounded Modes

Considering single-phase to ground (SPG) faults accounting for more than 80% of all faults, neutral ineffectively grounded mode (NIGM) is the most promising choice for medium voltage (MV) islanded microgrids (MGs) to enhance operation reliability. But due to the weak fault current and flexible network topology of islanded MGs, it is difficult to detect SPG fault lines. To solve this issue, this paper firstly analyzes the SPG fault characteristics of the MV islanded MGs with different NIGMs and network topologies. It is found that the looped MGs have a virtual terminal, i.e., the point where zero-sequence current (ZSC) is zero. And the feature differences between fault and non-fault lines are revealed. Based on that, an SPG fault line identification method is proposed. The main criterion is constructed based on the skewness signs of high frequency zero-sequence voltage (ZSV) and ZSC. Additionally, the fundamental amplitude of the difference between the actual ZSC received from another terminal and the ZSC calculated from local ZSV and ZSC is used to formulate an auxiliary criterion. Finally, the effectiveness of the proposed method is validated in a 10kV islanded MG model considering different NIGMs, different fault conditions, arc grounding faults, and the influences of the line parameter, operating point and measurement noises.

Single-phase grounding fault line selection method based on dynamic time warping algorithm

Aiming at the problems of the complex line selection process and slow line selection speed of the existing intelligent classification line selection methods, a single-phase grounding fault line selection method based on the dynamic time warping (DTW) algorithm is proposed. The wavelet modulus maximum truncates the original signal to extract the temporary part. Then the DTW distance matrix between feeders is figured by the DTW algorithm. The fault line selection is realized by comparing the difference between the maximum, sub maximum, and minimum values with the threshold value. The simulation shows that the proposed approach can recognize proper line selection under various fault states and partial loss of recorded data, has high accuracy and has better robustness than the Euclidean distance and person correlation coefficient methods.

A method of single-phase grounding fault line selection based on optimization spiking neural P systems

It is important to select the fault line rapidly when single-phase grounding fault occurs in the small current grounding system. The fault information acquisition of existing methods generally need hardware modification, so the high cost makes it difficult to apply to the power grid in underdeveloped areas. Taking that into consideration, this paper proposed a method of steady-state information small current grounding fault line selection based on Optimization Spiking Neural P Systems (OSNPS). The method only needs the steady-state voltage and current data of the dispatch side to effectively identify the fault line, which greatly improves the range of application. According to the characteristics of power dispatching big data, the objective function is established and the normalized model parameters are optimized by OSNPS to improve the accuracy of fault line selection stably. Furthermore, PSCAD/EMTDC is used to simulate the small current grounding system, the main factors affecting the accuracy of fault line selection are analyzed and the relationship between fault information features and fault identification accuracy is revealed. What’s more, It is pointed out that the model parameters without optimization may have line selection failure. Finally, specific examples are given to verify that the model parameters optimized by OSNPS can effectively improve the accuracy of fault line selection.

Research and Analysis of Small Current Grounding Line Selection System

In the small current grounding system, because the short-circuit fault of single-phase grounding accounts for more than 80% of the total short-circuit fault, the selection of single-phase grounding fault line is still the main problem in electrical engineering. This paper studies the principles, advantages and disadvantages of various line selection technical methods in case of single-phase grounding fault, and puts forward the improved method of comprehensive line selection for the problem of misjudgment of fault line selection. On this basis, it puts forward the improved method of comprehensive line selection, verifies the line selection device of 10kV transmission line through the relevant data obtained from design calculation and simulation experiment, and puts forward a more reasonable design scheme for the hardware circuit and software circuit of the line selection device, The fault tolerance and accuracy of small current grounding line selection system are improved.

Fa-Mb-ResNet for Grounding Fault Identification and Line Selection in the Distribution Networks

Accurate and fast identification of the fault types and the fault feeders can improve the distribution networks’ power supply reliability. This article focuses on two issues of classifiers in performing fault identification and line selection of the distribution networks, namely, the low utilization rate of fault information and the insufficient accuracy. We propose to use multilabel and multiclassification and build a fast-multibranch residual network (Fa-Mb-ResNet) to accomplish the identification and line selection of the distribution network grounding fault simultaneously. Our work has the following contributions. First, we propose a method of frequency division and time division for learning the features of the time–frequency matrix based on wavelet transformation. Second, we propose an improved residual unit (IRU) structure, which employs different small branches and convolution kernels to achieve the fusion of abstract fault feature information in different dimensions and enhance learning efficiency. Finally, the IRU structure is connected end to end. The new approach fully exploits the side fault information. Our extensive experiments show that the Fa-Mb-ResNet is faster, more adaptable, and has better anti-interference than the state-of-the-art methods in fault identification and line selection of the distribution network.

Fault Line Selection of Distribution Network Based on Modified CEEMDAN and GoogLeNet Neural Network

Aiming at the difficulty of single-phase grounding fault line selection in a small current grounding system, a distribution network fault line selection method based on modified CEEMDAN and convolutional neural network is proposed. Firstly, the random forest and multiscale permutation entropy are used to modify the Complete Ensemble Empirical Mode Decomposition Adaptive Noise algorithm (CEEMDAN), and the zero-sequence current of each line is decomposed into a series of intrinsic mode functions through the modified CEEMDAN (MCEEMDAN) algorithm. Secondly, the intrinsic mode function is transformed into the image formation by using the signal-image conversion method, and the generated image is upgraded to a three-dimensional color image by combining pseudo-color coding technology. Finally, the color images converted from the signals of each line are fused as the input of the GoogLeNet network, and the fault line selection of the distribution network is realized in the form of probability output by the Softmax function. The experimental results show that the proposed method has not only strong feature extraction ability and high recognition accuracy but also has good anti-noise and robustness.

Fault detection and classification of DC microgrid based on VMD

PurposeThis paper aims to present a new fault detection and classification scheme of both DC faults and AC faults on a DC microgrid network.Design/methodology/approachTo achieve reliable protection, the derivative of DC current signal is decomposed into several intrinsic modes using variational mode decomposition (VMD), which are then used as inputs to the Hilbert–Haung transform technique to obtain the instantaneous amplitude and frequency of the decomposed modes of the signal. A weighted Kurtosis index is used to obtain the most sensitive mode, which is used to compute sudden change in discrete Teager energy (DTE), indicating the occurrence of the fault. A stacked autoencoder-based neural network is applied for classifying the pole to ground (PG), pole to pole (PP), line to ground (LG), line to line (LL) and three-phase line to ground (LLLG) faults. The effectiveness of the proposed protection technique is validated in MATLAB/SIMULINK by considering different test cases.FindingsAs the maximum fault detection time is only 5 ms, the proposed detection technique is very fast. A stacked autoencoder-based neural network is applied for classifying the PG, PP, LG, LL and LLLG faults with classification accuracy of 99.1%.Originality/valueThe proposed technique provides a very fast, reliable and accurate protection scheme for DC microgrid system.

Research on Low-Resistance Grounding Fault Line Selection Based on VMD with PE and K-Means Clustering Algorithm

Aiming at the fault line selection problem in the single-phase grounding system of the distribution network, a new fault line selection method based on VMD and permutation entropy feature extraction combined with K-means clustering algorithm is proposed. This method is a hybrid algorithm that can effectively identify fault line selection. Firstly, a simulation model is built and its zero sequence current is collected. The variational modal decomposition method is used to decompose the collected zero-sequence current into multiple intrinsic modal functions, which can not only effectively reduce the influence of harmonic components and noise in the characteristic signal but also facilitate the calculation. The extracted intrinsic mode function is calculated by permutation entropy (PE), and the calculated entropy value is constructed into a matrix to highlight the fault characteristics of the line; then, the matrix is subjected to K-means cluster analysis through the preprocessing algorithm and the faulty line is correctly distinguished. Then, regression verification is performed. Finally, it is verified by the recorded wave data of the real test site and then analyzed and compared with other algorithms. The proposed method shows that when a single-phase ground fault occurs, the ground fault line selection can be effectively identified under different transition resistances, grounding resistances, and fault distances. Therefore, this method can accurately identify the fault line selection, and the accuracy rate is 100%, which has a certain use value.

Influence of wavelet characteristics on single-phase grounding fault line selection

It is difficult to select the fault line in distribution network because of the small single-phase grounding fault current. Wavelet transform is especially suitable for fault transient analysis because of its unique time-frequency localization performance. However, the characteristics of different wavelets have a direct impact on the line selection results. Improper selection of wavelets will directly lead to misjudgment. Firstly, the transient characteristics of single-phase grounding fault and the relationship between wavelet transform modulus maxima and singularity are analysed, and the line selection criterion of wavelet modulus maxima is given. Then, the influence of wavelet vanishing moment order, support length, symmetry, and orthogonality on the detection effect of signal singularity is analysed, and four principles for selecting wavelet in fault line selection are proposed. The larger the order of vanishing moment and support length, the more conducive to detection. The influence of vanishing moment order is much greater than that of support length. The 10kV system simulation model is built, five representative wavelets are selected to decompose the zero- sequence current of the fault line in four scales respectively, and the different modulus maxima corresponding to the sudden change point are compared. The experimental results prove the reliability of the wavelet selection principles proposed in this paper.

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

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