Distribution Network Fault Research Articles

Fault nature identification and location scheme for distribution network based on active injection from IIDG

In view of the difficulty in passively identifying the nature of faults in distribution networks and the high equipment cost of existing active detection schemes, a permanent fault identification and location scheme using an inverter-interfaced distributed generator (IIDG) connected to feeders for signal injection is proposed. After the faulty feeder is isolated, the power electronics in the IIDG are switched on for a short time and a voltage signal is injected into the feeder through the modified LCL filter and grid-connected transformer. The analysis shows that the aerial mode voltage of the feeder will not remain 0 under permanent faults. A highly sensitive criterion with fixed thresholds is constructed using approximate entropy (ApEn) to identify fault nature. Then the fault distance is further calculated based on the detected mutation point of the aerial mode voltage under permanent faults. The correctness of the theory and the effectiveness of the scheme are verified in PSCAD/EMTDC simulations.© 2017 Elsevier Inc. All rights reserved.

Design of Online Intelligent Detection System for Power Quality and Fault Identification in Distribution Networks

Abstract This paper lays the foundation for the subsequent analysis of power quality in distribution networks by briefly describing the indicators of power quality. Utilizing the zero sequence voltage mutation detection fault of a small current routing algorithm, the instantaneous values of voltage and current of each phase of the three-phase circuit are quickly calculated based on the electrical parameters of instantaneous reactive power and the online intelligent detection system is established. The effectiveness and feasibility of the designed detection system and detection algorithm in detecting electrical energy parameters have been verified through experiments. The results show that the error of voltage measurement is within 0.0003-0.002, the frequency error of the system is up to 0.0117%, the measurement error of three-phase unbalance is distributed between 0.001 and 0.1, and the error of negative sequence and zero sequence is between ±0.003, which meet the system design requirements. It shows that the use of online intelligent detection systems can improve the quality of distribution network operation and fault identification accuracy, improve power quality, and effectively improve the reliability of power supply.

Distribution network restoration supply method considers 5G base station energy storage participation

This paper proposes a distribution network fault emergency power supply recovery strategy based on 5G base station energy storage. This strategy introduces Theil's entropy and modified Gini coefficient to quantify the impact of power supply reliability in different regions on base station backup time, thereby establishing a more accurate base station's backup energy storage capacity model that can fully utilize the base station's energy storage resources to participate in the emergency power supply of distribution network faults. First, the optimal Copula function in each cycle is determined through the Akaike information criterion and squared Euclidean distance to establish a wind power-photovoltaic output scenario. Secondly, the traditional Gini coefficient is modified using the weighted average node degree and influence rate indicators. A comprehensive vulnerability model is established through the modified Gini coefficient and Theil's entropy indicators. Based on the comprehensive vulnerability model, a backup energy storage time model and a modified backup energy storage capacity model of the base station affected by power supply reliability are established. Thus, the callable energy storage capacity of base stations in different areas is obtained. Finally, a two-stage robust optimization model is introduced to minimize system operating costs to solve the volatility of 5G base station communications and wind-solar output, thereby establishing an emergency power supply recovery model for base station energy storage and wind-solar output. Simulated with the improved IEEE-33 node model, the results show that the proposed base station's energy storage model improves the utilization of the base station energy storage resources and, at the same time, effectively reduces the loss of load during the fault phase of the distribution network and improves the absorption of the PV output.

Research on the Physical Reproduction Method of Single-phase Grounding Fault in 10 kV Distribution Network Based on Digital Mirroring

It is necessary to construct a physical reproduction environment that conforms to the actual working conditions of single-phase grounding faults in 10 kV distribution networks, to effectively verify the single-phase grounding fault disposal capability of distribution automation equipment. In this article, a physical reproduction method of a single-phase ground fault in distribution networks based on digital mirroring is proposed. A digitized mirror model of a single-phase ground fault of a 10 kV distribution network is constructed. The system architecture for equivalent physical reproduction of single-phase grounding fault characterization is designed. Then the solutions to the critical technical problems in the realization are proposed. The reproduced single-phase ground fault signal was applied in the functional verification work of the first and second fusion distribution equipment. The experimental results show that the single-phase grounding fault in the 10 kV distribution network reproduced by this method comprehensively verifies the correct rate of the equipment for the single-phase ground fault disposal as well as the ability to prevent false action. Moreover, the reasons for the failure of the equipment s single-phase grounding fault disposal function were discovered.

Research on Confidence Analysis Method of Lightning Strike Accidents on Distribution Lines

Currently, lightning is the main cause of power system faults. Due to insufficient equipment and technology, low insulation level, the distribution network being the main part of power outages, and the complex causes of distribution network faults, it is difficult to determine whether each fault in the distribution network is caused by lightning strikes. In this paper, the concept of lightning strike confidence, i.e., the possibility of a line lightning strike fault caused by falling lightning, is proposed, and a lightning strike confidence analysis is performed for a 10 kV distribution line in the mountainous region of South China to determine the possibility of a line lightning strike fault caused by falling lightning based on the lightning locating system data of the distribution line at the moment of the fault. First, the calculation method of the lightning strike confidence is derived; then the distribution law of the distribution line lightning strike confidence, the influence of the ground tilt direction, and ground tilt angle on the distribution line lightning strike confidence are analyzed; finally, the distribution line lightning strike confidence is analyzed by an example.

Single Line-to-Ground Fault Type Multilevel Classification in Distribution Network Using Realistic Recorded Waveform.

The further identification of fault types for single line-to-ground faults (SLGFs) in distribution networks is conducive to determining the cause of grounding faults and formulating targeted measures for hidden danger treatment and fault prevention. For the six types of SLGFs generated in the actual power grid, this paper deeply studies their fault characteristics. Firstly, the classification criterion of fault transition resistance is derived by the generation mechanism of fault zero sequence voltage (ZSV). At the same time, by comparing and analyzing the same and different characteristics between faults, three criteria for fault classification are obtained. Based on the above four criteria, a multilevel and multicriteria fault classification method is proposed to judge six types of SLGFs. Then, the proposed method is verified by various fault state simulations of the distribution network model with a balanced topology and unbalanced topology. The engineering application of the method is demonstrated by the verification of actual power grid data. Finally, noise and data loss interference test results show the robustness of the method.

Mobile power sources pre‐allocation and dispatch strategy in power‐transportation coupled network under extreme weather

AbstractThis study introduces a strategy for the pre‐allocation and dispatch of mobile power sources (MPSs) within a power‐transportation coupled network, targeting efficient distribution network fault recovery under extreme weather conditions. The strategy incorporates the Dijkstra algorithm and blind number theory to analyse the MPS driving path and generate fault scenarios respectively, considering the effects of extreme weather on traffic capacity and distribution network fault rate. The MPS pre‐allocation model, formulated to minimize the load outage loss, MPS investment, and driving fuel cost, provides a foundation for optimization of the dispatch process. The dispatch optimization model adapts to dynamic information from the power‐transportation coupled network to make decisions on load restoration sequencing and MPS driving path. The effectiveness of the proposed strategy is demonstrated through an application to the IEEE 33‐node distribution system and a corresponding 33‐node transportation network.

Quantum annealing algorithm for fault section location in distribution networks

As the last link of the power system, the distribution network is responsible for ensuring stable power consumption and improving power quality. Therefore, a more reliable and fast fault section location(FSL) method is essential for the stable operation and optimization of distribution networks. In this context, this paper adopts an effective method to apply the quantum annealing algorithm(QA) based on the quantum tunneling mechanism to the distribution network fault section location problem. A quantum Hamiltonian function consisting of potential and kinetic energy terms is constructed based on the theoretical knowledge of QA. Among them, FSL objective function is mapped to the potential energy term, and the transverse magnetic field is introduced to construct the kinetic energy term, which can realize the quantum tunneling effect and approximate or even reach the global optimal solution. Based on the quantum Hamiltonian function construction, this paper modifies some parameters in the QA framework to propose an improved quantum annealing algorithm(IQA) to improve the accuracy. In the two test systems of IEEE 33-node distribution network and IEEE 33-node distribution network with distributed generation sources(DGs), QA and IQA are compared and analyzed with other intelligent algorithms using the average number of iterations and localization accuracy as indicators. We find that QA is more likely to obtain the global optimal solution compared with the simulated annealing algorithm(SA). IQA can search for faulty sections with 100% accuracy and the least number of average iterations in both single power distribution networks and distribution networks containing DGs. Under the scenarios of fault signal distortion and increasing fault sections, IQA shows superb competitive advantages by exhibiting good fault tolerance performance, global optimal search capability and stability.

Research of Islanding Operation and Fault Recovery Strategies of Distribution Network Considering Uncertainty of New Energy

With the problems of fault handling in the distribution network, few studies concern the correlation between islanding operation and fault recovery. Thus, this paper proposes an islanding operation and fault recovery strategy for the distribution network considering the uncertainty of new energy. Firstly, the objectives of the distribution network islanding division scheme and operation optimization are established. Combined with distribution network radiation constraints, islanding power supply capacity, safety constraints, and distributed generator (DG) operation constraints, a rolling optimization method is used to construct the distribution network islanding division and operation model. Secondly, in the fault recovery stage, considering the characteristics of the island operation stage, a node load weight value is designed. A distribution network fault recovery model is then constructed with the goals of ensuring greater load power supply recovery, improving electricity satisfaction, and reducing network losses and switching times. Thirdly, considering the randomness of intermittent DGs such as wind power and photovoltaics, an uncertainty model of intermittent DGs is constructed. A solution method for the distribution network islanding operation and fault recovery model considering uncertainty is proposed by combining scenario generation reduction methods and second-order cone programming theory. Finally, the proposed method’s feasibility and effectiveness are verified using the improved IEEE 33-node distribution network. The results show that in the islanding division stage, the node voltage consistently remains between 1.08 pu and 1.1 pu when new energy achieves up to 34.09% and 48.65%, and the line losses represent approximately 0.22% to 0.26% of the total load when the initial energy levels of the storage are at 50% and 80%. In the fault recovery stage, compared to the method without network reconstruction, the system shows significant power loss reductions of approximately 11.9%, 13.6%, and 14.2% in three respective cases.

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.

A New Approach to Detect Power Quality Disturbances in Smart Cities Using Scaling-Based Chirplet Transform with Strategically Placed Smart Meters

The growth of Internet of Things (IoT)-enabled devices has increased the amount of data created by the distribution network’s periphery nodes, requiring more data transfer capacity. Recent applications’ real-time requirements have strained standard computing paradigms, and data processing has struggled to keep up. Edge computing is employed in this research to detect distribution network faults, allowing for instant sensing and real-time reaction to the control room for faster investigation of distribution problems and power outages, making the system more reliable. Moreover, to overcome the challenges of fault detection, advanced signal processing methods need to be integrated with the Adaboost classifier. An Adaboost-based edge device, suitable for installation on top of a power pole, is proposed in this research as a means of real-time fault detection. To increase throughput, decrease latency and offload network traffic, data collecting, feature extraction and Adaboost-based problem identification are all performed in an integrated edge node. Enhanced detection accuracy (98.67%) and decreased latency (115.2 ms) verify the effectiveness of the suggested approach. In this research, we enhance the classical chirplets transform to create the scaling-basis chirplet transform (SBCT) for time–frequency (TF) analysis. This approach modulates the TF basis around the relevant time function to modify the chirp rate with frequency and time. By carefully selecting the sampling frequency, it is possible to discriminate between short circuit fault and high-impedance fault (HIF) by calculating spectral entropy. The TF representation obtained with the SBCT provides considerably higher energy concentrations, even for signals with numerous components, closely spaced frequencies and heavy background noise.

Flexible DC distribution network fault location method based on beetle swarm optimization algorithm

To improve the fault location ability of the DC distribution network and ensure its power supply reliability, a fault location method based on the beetle swarm optimization algorithm is proposed. Firstly, the fault characteristics of the DC side after short-circuit fault are analyzed, and the mathematical model for fault location is established; then, the problem of fault location is transformed into the problem of parameter identification by discretization; finally, to verify the effectiveness of the method, a flexible DC distribution network model is built in MATLAB. The simulation results show that the proposed fault location method is accurate, not affected by the transition resistance, and has good robustness.

Research on the identification of high-resistance ground faults in the flexible DC distribution network based on VMD–inception–CNN

With the rapid development of flexible DC distribution networks, fault detection and identification have also attracted people’s attention. High-resistance grounding fault poses a great challenge to the distribution network. The fault current is very small and random, which makes its detection and identification difficult. The traditional overcurrent protection device cannot identify and act on the fault current. Therefore, this paper proposes a fault detection method based on variational mode decomposition (VMD) combined with the convolutional neural network (CNN) of the inception module. This method first uses VMD to decompose the positive transient voltage. Second, it inputs the decomposed signal into CNN for training to obtain the optimal parameters of the model. Finally, the model performance is tested based on the PSCAD/EMTDC simulation platform. Experiments show that the detection method is accurate and effective. It can realize the accurate identification of seven different fault types.

A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks

AbstractThe structure complexity and the extended geographical area are among the factors that create challenges in accurate fault location and detection in distribution networks. The single‐phase to ground (SPG) faults are considered as most frequent reasons for distribution network interruption, which can threaten the network's reliability. Signal processing methods are usually used as common approaches to distinguish and locate faults in distribution networks. This paper proposes a novel scenario to select optimal wavelet packet transform (WPT) coefficients for SPG fault location and a method based on superimposed voltage energy to distinguish the faulty phase according to these coefficients. Furthermore, employing the energies of the superimposed voltages helps to minimize the effects of high resistance faults (HRFs) and load encroachment on the efficiency of the faulty phase detection part. Comparing the obtained results with other scenarios demonstrates the considerable efficiency of the proposed scenario. Finally, with the help of general regression neural networks (GRNN) as machine‐learning tools, a new algorithm is derived for detecting and locating the SPG fault and capacitor bank switching overvoltage (COV). Simulation results are implemented on the IEEE 34‐bus standard distribution network, demonstrating the efficiency and superiority of the suggested method.

Faulty-Feeder Detection for Single Phase-to-Ground Faults in Distribution Networks Based on Waveform Encoding and Waveform Segmentation

Faulty feeder detection helps ensure the stability and safety of power grids after single-phase-to-ground (SPG) faults occur in distribution networks. The existing detection techniques identify the faulty feeder by extracting representative fault features, while they fail to show reliable detection performance due to variable fault conditions and complex fault transients. To address these drawbacks, this paper proposes a method based on waveform encoding and waveform segmentation. Since the waveforms have complete fault features in fault signals, it is suitable to recognize the signals on the waveform scale, rather than extracting and fusing several fault features. Firstly, raw sampled zero-sequence voltage (ZSV) and zero-sequence current (ZSC) are processed by using the proposed encoding method, and the ZSV-ZSC image can be generated quickly. Secondly, to learn and understand the waveforms of ZSV and ZSC, a two-path fully convolutional network (FCN) is established to make pixel-wise prediction on the ZSV-ZSC image. Finally, the fault degree of each feeder can be estimated based on the segmented waveform in the ZSV-ZSC image. The performance evaluation is implemented in the NVIDIA Jetson Xavier embedded platform, and the experimental results demonstrate that the proposed method can identify the faulty feeder with high accuracy within 28 ms.

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.

Detection of single-phase-to-ground faults in distribution networks based on Gramian Angular Field and Improved Convolutional Neural Networks

Fault detection of transmission lines is a fundamental guarantee for the stable operation of the power grid. The characteristic information is not obvious when a single-phase-to-ground fault occurs in the complex distribution network, and the existing methods are vulnerable to the problems of fault conditions and environmental noise. To address this problem, a new method of distribution network fault diagnosis is proposed based on Gramian Angular Field (GAF) and Improved Convolutional Neural Networks (ICNN) in this paper. Firstly, the time-series signals are normalized and transformed to a polar coordinate system. The encoded signals are converted into visual virtual images through the inner product calculation of GAF algorithm. Secondly, multi-scale module and attention mechanism are added to the convolutional layer of the ICNN. Among them, the multi-scale module, which enhances the local feature extraction capability for the original image, is composed of multiple convolutional channels. The purpose of attention mechanism is to help the weight distribution and depth feature filtering of ICNN, focusing on the key features between fault data. Finally, the transfer learning method is further introduced to improve the fault diagnosis model performance for different online situations. The experimental results show that the overall accuracy of the proposed fault location method is close to 99% under different topological situations. Even with the influence of ambient noise, the accuracy of GAF-ICNN still approaches 97.5%.

Research On Multi-source Collaborative Distribution Network Fault Recovery Technology

When a power outage occurs in the power system, multiple distributed power sources and energy storage systems connected to the distribution network can be coordinated to restore power to important lost loads in a priority and fast manner. However, the generation output of intermittent power sources such as wind and photovoltaic is uncertain, which makes it difficult to provide a continuous and stable power supply to the loads in isolated islands. In this paper, we first explore the improvement effect of multi-source cooperative fault recovery on distribution system resilience, consider the distribution system with distributed power sources, energy storage systems, and controllable loads, and propose a distribution system recovery model with the goal of maximizing the power consumption of the restored loads for a relatively short period of time after a power outage incident. Then, the power output characteristics and power supply capacity of various distributed power sources are analyzed, and the intermittent power output fluctuations are compensated using controllable loads. Finally, the proposed model and method are validated using a modified IEEE 33-node distribution network as an example.

An integrated risk assessment model for the multi-perspective vulnerability of distribution networks under multi-source heterogeneous data distributions

The reliability of distribution network transmission corridors are vulnerable to different risk factors in both internal operation parameters and surrounding environmental conditions, and thus pinpoint evaluations of these impacting factors can further enhance their safety. To this end, this paper proposes an integrated assessment method, that is the multi-dimensional assessment with integrated fuzzy association (MDATFA). In this model, healthiness and importance indices of transmission lines are both defined to reflect the self-robustness and potential damages, respectively, and therefore to enable a two-dimensional assessment to incorporate the multi-source heterogeneous inputs. Firstly, healthiness indices for environmental characteristics are analyzed. On the one hand, discrete input features are processed by the established association rule mining model with rare variables (ARMrv) to mine and diagnose rarely occurred elements, and their healthiness contribution values can then be calculated. On the other hand, continuous features are handled by the designed hierarchical probabilistic fuzzy systems (HPFS). The weight assignments of these two expert models are adjusted by a gating network. Secondly, the importance indices for electrical characteristics are calculated. Under the condition that electrical signal constraints are satisfied, the amount of load loss and lost users can be solved by the built distribution network fault recovery model which is according to heuristic rule and second-order cone (HRSO). Finally, the validity and robustness of this operational condition assessment model is verified by an empirical analysis in an actual 39-bus power distribution system.

Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network

With many branch lines in radiant distribution networks, diagnosing faults in a distribution network is very difficult. It is of great significance to identify different types of faults quickly and accurately for the stable operation of the power grid. This research presents a fault identification model for a distribution network based on artificial neural networks. The principal component analysis first extracts features from transitory data in a distribution network. The resulting low-dimensional data is subsequently used to update the artificial neural network model. The artificial neural network may also identify the type of fault. The proposed model’s fault detection accuracy is improved over the traditional approach by examining distribution network fault data during the simulation test.