Transient Zero-sequence Current Research Articles

Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks

Fault localization is crucial for ensuring stability, particularly in high impedance faults (HIF) characterized by low current levels and prolonged transient processes (TP). Existing methods predominantly analyze differences in the fixed-length transient waveform, potentially causing delays in triggering or failure in HIF scenarios. To address these challenges, a novel AI application paradigm for HIF localization was introduced, incorporating both adaptive TP calibration and multiscale correlation analysis. Based on 1D-Unet, the TP of the zero-sequence voltage (ZSV) can be adaptively calibrated to maximize the utilization of transient information. Subsequently, the differential zero-sequence voltage (DZSV) and transient zero-sequence current (TZSC) can be acquired to facilitate multiscale correlation analysis. Combined with a sliding window strategy, the micro correlation between DZSV and TZSC is articulated through the local correlation degree (LCD). The comprehensive correlation degree (CCD) between DZSV and TZSC is then formulated to realize fault feeder/ section localization at the macro level. The 1D-Unet model achieved a classification accuracy of 99.2 % for sample points in test datasets and showed robustness with an accuracy exceeding 93.5 % in the presence of 20 dB noise interference. When integrated with the well-trained 1D-Unet, the proposed approach underwent further validation using simulation data and field recordings. These tests confirmed the model's resilience to noise interference up to 20 dB and its efficacy across networks of diverse topologies, such as the IEEE-13 and 34-node distribution networks. Additionally, an industrial prototype applying this framework identified all fault conditions without false positives or omissions, outperforming existing methods under various fault scenarios, including those involving high impedance materials and different resistance levels across multiple feeders.

A visual faulty feeder detection method for power distribution network based on spatial image generation and deep learning

AbstractIn the case of a single‐phase grounding fault in the distribution network, the transient zero‐sequence current (TZSC) tends to be non‐linear and non‐stationary. The faulty line selection is relatively difficult. The distributed power access further brings many difficulties to faulty line selection. This work proposes a novel method of faulty line selection using spatial image generation and deep learning. At first, the optimal smooth denoising model can be used to smooth the zero‐sequence current for each line, reducing the external environment electromagnetic interference. Then, the treated zero‐sequence current is mapped into the colorful floral hoop image by using symmetrized Hilbert transform pattern (SHTP). The SHTP transforms the one‐dimensional time domain signal into the two‐dimensional space domain image, enhancing invisible information and obtaining more abundant feature information. Finally, the deep features of the SHTP floral hoop image are extracted by means of deep learning method. In order to improve the faulty line selection universality, a mixed sample library containing three different topologies is established, including the 10 kV radial distribution network, IEEE‐13 node model, IEEE‐34 node model and StarSim platform. The comparisons show that the proposed method has a more noticeable visualization effect on fault features, higher classification precision rate, and better anti‐noise performance.

A faulty feeder selection method for distribution network with unintentional resonance in zero sequence circuit

Faulty feeder selection is a challenging task for distribution system operator due to the low earth fault current magnitudes in compensated networks. However, extensive cable usage, especially in metropolitan cities, causes unintentional resonance in the network earthed through inductance or grounding transformer. The unintentionally resonated networks are not designed like intentionally compensated networks where faulty feeder should be isolated in a predetermined time. There are transient zero sequence current based methods, particularly synthesized for compensated networks to identify faulty feeders. However, zero sequence-based faulty feeder selection methods have drawbacks in the presence of underground cables. Further, transient zero sequence current is prone to many parameters such as capacitive imbalance and fault resistance.In this study, a transient negative sequence current based faulty feeder selection method is proposed. The effectiveness of the proposed method is demonstrated in a simulated 151-node distribution network. EMTP simulations are carried out by considering different fault inception times, fault resistance and capacitive imbalance of the system. Results show that negative sequence current offers selective faulty feeder selection and no false trip is observed in a representative unintentionally resonating distribution network.

Faulty Feeder Detection Based on Grey Correlation Degree of Adaptive Frequency Band in Resonant Grounding Distribution System

Due to the particularity of their process, petrochemical enterprises have high requirements for the reliability of power supply. If a large-scale blackout occurs due to a grounding fault, it will pose a huge threat to safe production. When the resonant grounding system of petrochemical enterprises faults, due to the complex fault process and weak fault signal, it is difficult to accurately detect the faulty feeder by traditional methods. This paper presents a new method of grey correlation degree based on adaptive frequency band. Firstly, the transient zero-sequence current of each feeder is decomposed by coif5 wavelet, and the low frequency band a5 (power frequency component) and high frequency band d1, d2 (noise signal) are removed. By stacking all of the remaining frequency band signals to construct the wavelet area matrix, the faulty feeder detection characteristic scale and the first faulty feeder detection result are obtained. Secondly, based on the faulty feeder detection characteristic scale, the second faulty feeder detection result is obtained by the average grey correlation degree matrix, which detects the faulty feeder according to the waveform correlation degree. Finally, the final faulty feeder detection result is obtained by equal weight voting. In MATLAB/Simulink, the 10 kV resonant grounding system of petrochemical enterprises is modeled. A large number of simulation results show that the faulty feeder detection method is not affected by the initial phase angle (0°, 45° and 90°), transition resistance (10 Ω, 100 Ω and 1000 Ω), fault distance (1 km, 8 km and 15 km) and overcompensation degree (5%, 8% and 10%), and has good sensitivity.

A Faulty Line Selection Method for Flexible Grounding System Based on Transient Fault Data

In view of the problem of high-impedance grounding faults (HIFs) in flexible grounding systems (FGS), a faulty line selection method based on transient waveform slope is proposed in this paper. The change of zero sequence (ZS) current of faulty line and non-faulty line (before and after the neutral point parallel small resistance are put in) are analyzed. After the parallel small resistance is put into operation, the waveform slope curve of the transient zero sequence current of each line is obtained, and the modified Hausdorff distance (MHD) algorithm is used to obtain the transient slope matrix. The faulty line is identified by comparing the sizes of each row of elements in the matrix. Simulation verifies the effectiveness of the proposed faulty line selection method.

Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems

Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accuracy and strong robustness compared with existing fault detection methods.

Faulty Feeder Identification Method Considering Inverter Transformer Effects in Converter Dominated Distribution Network

With the increase in inverter interfaced distribution generations (IIDGs), converter-dominated distribution networks (CDDNs) are expected. When a single-phase-to-earth (SPE) fault occurs in a CDDN, since the inverter transformer effects can affect the transient fault characteristics of fault signals, the existing faulty feeder detection techniques may not be applicable. This paper focuses on the inverter transformer effects on transient zero sequence currents (TZSCs). First, the inverter transformer effects on TZSCs are analyzed, and the analytical expressions of TZSCs are derived. It is found that the transient fault characteristics in TZSCs may become weak by considering the inverter transformer effects. As a result, the accuracy and reliability of faulty feeder detection face more challenges. To address this issue, a novel faulty feeder identification method based on the first order accumulated generating operator (AGO) method and improved cosine similarity is proposed. The effectiveness and applicability of the proposed method are validated in the converter-dominated distribution network. Additionally, the field test results also indicate the feasibility of the proposed method.

A faulty line detection method for single phase-to-ground fault in resonant grounding system with CTs reversely connected

Single-phase grounding (SPG) faults often occur in distribution systems but are not well resolved in resonant grounding systems due to their complexity, particularly when zero-sequence current transformers (CTs) are connected in the reverse direction. This paper presents a faulty line detection method that is adapted to the reversal of current transformers. First, the zero-sequence equivalent model of a single-phase grounding fault is analyzed, and according to the healthy line equation, it can be deduced that the zero-crossing time of the transient zero-sequence current of the line is the same. Second, the transient zero-sequence current zero-crossing time difference between the faulty line and the healthy line is analyzed using Monte-Carlo simulation, and it is concluded that the absolute value of this difference is less than 1 ms in fewer cases. Finally, the zero-crossing time difference between the lines is used to construct the total timing sequence coefficient (TTSC). When all TTSC is less than 1 ms, the bus is faulty; otherwise, the line with the largest TTSC value is the faulty line. The test of the method is completed on the radial distribution network, laboratory data, field test data, and arc wave recording data. The results of extensive validation show that the proposed method has good performance and can detect faulty lines when the current transformer is reversed.

Faulty Feeder Detection for Single-Phase-to-Ground Fault in Distribution Networks Based on Transient Energy and Cosine Similarity

When a single-phase-to-ground (SPG) fault occurs in distribution networks, the existing faulty feeder detection techniques do not present reliable detection results. This paper introduces a novel faulty feeder detection method based on transient energy and cosine similarity. First, the fault characteristics of transient zero-sequence current (TZSC) are analyzed. It is found that the fault transient characteristics of transient components decrease as the grounding resistance increases. Second, to fully maintain the fault transient features, a fundamental frequency component (FFC) shifting method is introduced to remove FFCs. Next, the transient currents without containing FFCs are utilized to generate two fault indicators: transient energy within the first half power cycle and cosine similarity after the first half power cycle. After that step, to effectively merge the two fault indicators, the Laplace distribution is utilized. The fault possibility of each feeder can be quantitatively estimated by calculating the cumulative density function (CDF). Finally, the feeder with the maximum fault possibility is determined as the faulty feeder. The effectiveness and applicability of the proposed detection method are verified in the radial distribution network and the modified IEEE 34-bus distribution network. The results of the field test also indicate the feasibility of the proposed SPG detection method.

Fault Line Selection with Correlation Analysis of Zero Sequence Current and Voltage under High-Low Frequency

When a single-phase ground fault occurs near the zero or maximum point, the fault line selection of resonance grounding system is not sensitive enough. After analyzing the transient current and line impedance characteristics of zero sequence network, and making full use of the decaying DC component, power frequency component and high frequency component of the transient zero sequence current of the feeder, a fault line selection method based on correlation analysis of zero sequence current and bus zero sequence voltage derivative in high and low frequency band is proposed. The effectiveness of the method is verified by the examples from the aspects of fault closing angle, transition resistance.

A single-phase-to-ground fault location method based on convolutional deep belief network

After single-phase-to-ground fault (SPGF) occurs in the resonant grounding system, it will produce transient electrical quantities with abundant characteristics, such as transient zero-sequence current, transient three-phase current, and transient zero-sequence voltage. At this point, there is a great discrepancy between transient zero-sequence current and transient three-phase current sudden-change at upstream and downstream detection nodes, while those at detection nodes on the same side are similar. Besides, the trend of zero-sequence current at upstream detection nodes and zero-sequence voltage is opposite, while the trend of them downstream is similar. On this basis, this paper presents a new method for locating SPGF, which is based on the convolutional deep belief network (CDBN). Firstly, transient zero-sequence voltage, transient zero-sequence current, and transient three-phase current sudden-change are decomposed and reconstructed by discrete wavelet packet transform (DWPT) to obtain a time-frequency matrix. Then, taking it as input of CDBN to autonomously extract fault characteristics, and fault detection nodes are divided into upstream and downstream detection nodes. Finally, combined with network topology, fault location can be realized. The simulation test and data analysis show this method can identify fault segments accurately and reliably, and has good adaptability and application value.

Grounding fault test technology of distribution network based on zero sequence power direction judgment

Distribution network grounding fault testing technology relies on a single criterion, which has a positioning blind area, resulting in large ranging error. In this paper, a grounding fault test technology for distribution network based on zero sequence power direction determination is proposed. The direction from the power supply side to the load terminal is specified as the positive direction of the network, the fault equivalent zero sequence current is modeled, and the high-frequency component of transient zero sequence current containing main fault characteristics is extracted. The fault line is determined based on the zero sequence power direction. For the fault line, the zero sequence power is less than zero, and the absolute value is much greater than the normal line, and the line selection device can select the line normally. The grounding fault test model of distribution network is established, and the fault current difference of each measuring point is calculated. When the difference is the smallest, it is identified as the fault location. The average ranging error of the test technology based on zero sequence power direction determination is 50.68m, which is 20.24m and 23.55m lower than that based on wavelet analysis and EMD. Therefore, it can accurately determine the fault line and has strong positioning advantages.

Faulty Line Selection Method Based on Comprehensive Dynamic Time Warping Distance in a Flexible Grounding System

A flexible grounding system is a system in which the neutral point of the power supply is grounded via the arc suppression coil in parallel with a low-resistance resistor. When operating normally or a temporary ground fault occurs, the arc suppression coil is used for grounding, whereas the small resistance is switched on when a permanent ground fault occurs. At present, the problem of low protection sensitivity when a high-resistance ground fault occurs in a flexible grounding system has not been solved yet. According to the characteristics of low waveform similarity between the faulty line and the non-faulty line when a single-phase grounding fault occurred, a new faulty line selection method based on a combination of Dynamic Time Warping (DTW) distance and the transient projection method is proposed in this paper. Firstly, the fault transient signal is extracted by a digital filter as a basis for faulty line selection. Secondly, the transient zero-sequence current of each line is projected onto the busbar transient zero-sequence voltage, and the projected DTW distance of each line is calculated. Finally, according to the calculation formula of waveform comprehensive similarity coefficient, the Comprehensive DTW (CDTW) distance is obtained, and the top three CDTW distance values are selected to determine the faulty line. If the maximum value is greater than the sum of the other two CDTW distance values, the line corresponding to the maximum value is judged as the faulty line; otherwise, it is judged as a busbar fault. The simulation results based on MATLAB/Simulink and field data test show that the method can accurately determine the faulty line under diverse fault conditions.

Single-phase-to-ground Fault Line Selection Method Based on STOA-SVM

In view of the complex characteristics of nonlinearity and non-stableness of the zero-order current of each line after the single-phase ground fault of the distribution network, a distribution network fault selection method based on Sooty Tern Optimization Algorithm(STOA) and the combination of support vector machine is proposed. At first, the zero-sequence current before and after fault is obtained, then five kinds of IMFs including different components are obtained by ensemble empirical mode decomposition, and the energy entropy of the fault transient zero-sequence current is obtained by Hilbert transform, the results of training and testing are obtained by inputting the feature vector. The simulation results show that the accuracy of the proposed line selection model is 97.5%.

Small Current Fault Line Selection Based on Improved Empirical Mode Decomposition and Fractal Dimension Method

In this paper, the fault steady state and transient characteristics of small current grounding system are analyzed, and the distribution of transient zero sequence current is introduced. A fault line selection based on EMD and fractal dimension method is proposed. After the parameter is determined, the problem is proposed and improved. Using the simulated annealing K-means algorithm to find the scale-free interval curve to get the line slope is the correlation dimension of the line. Finally, by comparing the size of the associated dimension, you can select the corresponding line of the faulty distribution network.

Single-phase Earth Fault Section Location Based on Differences’ Characteristics of Zero-sequence Currents in Distribution Network

When a single phase earth fault occurs in distribution network, the difference between the transient zero-sequence currents at the head of the non-faulty section before the fault point is small, and the non-faulty section after the fault point also has this feature, but the difference between the zero-sequence currents at the head of the section before and after the fault point is big. Based on this, a method for comparing the single-phase grounding fault section location in distribution network based on the difference of the transient zero-sequence current was proposed. Based on the known distribution network structure, the fault section table was compiled, then the transient zero-sequence currents difference at the head of each section were compared, the fault section could be selected. The simulation by PSCAD/EMTDC verified the reliability of the method.

High-Impedance Fault Detection Method Based on One-Dimensional Variational Prototyping-Encoder for Distribution Networks

Designing a neural network using simulation waveforms for high-impedance fault (HIF) detection is a challenging task, particularly in case of applications on distribution networks. Based on the characteristics of HIF transient zero sequence currents (TZSCs), a novel methodology is proposed for HIF detection in the time domain, which includes 1-D variational prototyping-encoder (1-D-VPE) and decision tree (DT) algorithm. The proposed HIF detection scheme consists of four steps: First, the TZSCs collected from simulations are applied to train the 1-D-VPE neural network, whose encoder is used as the feature extractor. Then, the extracted features are utilized to train the DT classifier. After that, the well-trained encoder and DT are employed to test field TZSCs. Finally, the 1-D-VPE neural network is fine-tuned with some of the field TZSCs. The performance of the proposed method is analyzed using waveform samples collected from the PSCAD/EMTDC simulation platform and the actual distribution networks. The results show that the proposed method provides good classification performances on both simulation and field data at high noise levels without transformation between different domains, and the classification accuracy can be improved by fine-tuning with some field TZSCs.

Faulty Feeder Detection Based on Fundamental Component Shift and Multiple-Transient-Feature Fusion in Distribution Networks

When a single-phase-to-ground (SPG) fault occurs in a distribution network, the current is weak, and the fault conditions are complex; the existing fault detection technologies do not show reliable performance. Unlike the existing detection methods, we introduced a fundamental component shift and multiple-transient-feature fusion method in this article. To remove the fundamental component (FC) that contained in the transient zero-sequence current (TZSC), Hilbert transform was used to calculate the analytic signal of TZSC. Then, the shift factor was introduced to remove the FC such that all the transient features can be kept. Second, three typical transient features indexes were calculated. Then, the multiple evidence estimation method was used to fuse the transient features indexes. Finally, selected the feeder with the maximum fault degree as the faulty feeder. To verify the accuracy and extensive applicability of the proposed method, developed the sample radial distribution network model with different fault conditions, arc fault model, and the modified IEEE-34-node test system with power electronics generator and wind energy conversion systems, the proposed method shows good performance.

Faulty Feeder Detection Method Based on VMD–FFT and Pearson Correlation Coefficient of Non-Power Frequency Component in Resonant Grounded Systems

Through analyzing the transient components and transient characteristics in transient zero-sequence current (TZSC), a novel fault feeder detection method based on the transient correlation of non-power frequency components (NPFCs) for the resonant grounded system is proposed. Firstly, using variational mode decomposition combined with fast Fourier transformation (VMD–FFT) to decompose the TZSC, by removing the power frequency components and noise signals, the transient NPFCs can be obtained. Secondly, to reflect the overall changing trend between faulty and healthy currents, the moving average filter is introduced to smooth the NPFCs; in this way, the fault transient features can be accurately revealed. Finally, the faulty feeder can be detected by comparing the threshold with the maximum difference value of comprehensive correlation coefficient of NPFCs. The detection results show that the proposed fault detection method can accurately select the faulty feeder; it is unaffected by fault resistances, fault phase angles, etc. Moreover, the detection method can resist noise interference.

Location of Single-Line-to-Ground Fault Using 1-D Convolutional Neural Network and Waveform Concatenation in Resonant Grounding Distribution Systems

Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location. However, it is still a challenge to extract effective features from massive data for single-line-to-ground (SLG) fault-section location. This work proposes a novel method of fault-section location using a 1-D convolutional neural network (1-D CNN) and waveform concatenation. After SLG fault occurs, DFI measures the transient zero-sequence currents at double-ends of the line section, which could be concatenated to construct characteristic waveform. The features of characteristic waveforms would be extracted adaptively by 1-D CNN to locate the fault section. Furthermore, the problem where the on-site recorded data are hard to collect would be solved because 1-D CNN only needs a small number of samples for training in practical applications. The experimental results verified that the proposed method could work effectively under various fault conditions, even if a few DFIs are out of order.