Fault Location In Distribution Networks Research Articles

A Novel DWT-ERT-based Fault Location for Distribution Network

Background: A new DWT-ERT-based fault location method is suggested in the IEEE test feeder. Objective: The fault location approach in the distribution network has been proposed in this pa-per that utilizes the discrete wavelet transform (DWT) and ensemble regression tree (ERT). Methods: The fault location methodology has been validated by simulations conducted on an IEEE 13 bus node test feeder. Results: The results show that the suggested solution has low compute burden and memory re-quirements, and is unaffected by system and fault situations. Conclusion: In this study, the fault location approach for the distribution system employing DWT and ERT has been proposed.

Low-voltage characteristic voltage based fault distance estimation method of distribution network

The traditional medium-voltage distribution network fault location method uses mainly the voltage and current measurements of the medium-voltage side, which results in problems such as high installation costs at the measuring points and complicated postoperation and maintenance work. Therefore, a fault location idea based on the distributed measurement of low-voltage side voltage is proposed in this paper. First, the characteristic voltage is adaptively selected according to the fault type. Second, the suspected fault section is determined by comparing the characteristic voltage amplitude of each measuring point. Third, the fault section is located using the section unit characteristic voltage drop defined for each suspected fault section. Finally, fault distance estimation is achieved based on the voltage difference matrix and characteristic voltage analysis. This method achieves accurate fault distance identification based on the distribution difference of the characteristic voltage of the low-voltage side under the fault state. This work provides a new economical and practical idea for determining the fault locations of distribution networks. The effectiveness of this method is evaluated by considering a 10 kV distribution network in Guangdong Province built in PSCAD/EMTDC.

Earth Fault Location for Non-Directly Grounded Distribution Networks

Earth Fault Location for Non-Directly Grounded Distribution Networks

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points.

A novel fault location method for the active distribution network based on dynamic quantum genetic algorithm

AbstractThe fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit.

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

A new method for estimating fault location in radial medium voltage distribution network only using measurements at feeder beginning

A new method for estimating fault location in radial medium voltage distribution network only using measurements at feeder beginning

Line fault location of flexible DC distribution network based on adaptive noise empirical mode decomposition

A fault location method for flexible DC distribution network lines is proposed to address the issues of insufficient decomposition in fault signal extraction and the complexity of traveling wave head detection. The method combines adaptive noise empirical mode decomposition (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN is used to adaptively denoise the line mode signal and obtain the optimal modal component to overcome modal aliasing and insufficient decomposition in signal separation. Then, the reach time of the fault traveling wave is determined by using TEO. Ultimately, the fault location is accurately determined by employing both ends of the traveling wave ranging technique. Through the combination of software PSCAD and MATLAB, the simulation experiment of the line fault is carried out. This means that the method can quickly and precisely identify the location of faults in a system.

Application of QGA algorithm improved by gradient descent in fault diagnosis and location of distributed distribution network

AbstractA fault diagnosis and localization approach for distributed distribution networks is created using an upgraded quantum genetic algorithm to swiftly identify and detect flaws in the network. In this method, the dynamic rotation strategy in gradient descent method is used to update the quantum gate to enhance the convergence speed, that is, the gradient descent quantum genetic algorithm is constructed. The results of single fault and multiple fault simulation test on the distribution network model of regional node of distributed power supply show that the average iteration of gradient descent quantum genetic algorithm 85.36, 86.35, 88.24, and 88.69 times can reach the target optimal value. In four different cases, the algorithm of gradient descent quantum genetic algorithm can reach the optimal by iterating 88, 91, 92, and 90 times, respectively. Compared with other algorithms, the convergence rate of gradient descent quantum genetic algorithm is the fastest in the four experimental cases. The consistency between the output score and the real score of the gradient descent quantum genetic algorithm is above 0.9. The results above show that the algorithm is effective. The optimization ability and stability of the algorithm are also stronger, and it has certain application potential.

A New Fault Distance Measurement Scheme for DC Distribution Network

To solve the problem of fault location in the DC distribution network, this paper proposes a new fault distance measurement scheme based on the impedance measurement algorithm against fault transition resistance and the improved Moore voting algorithm. Firstly, signals of different frequencies are injected into the DC circuit in different periods, after the converter is blocked due to the DC side fault. Then, through the distance measurement algorithm against fault transition resistance, several groups of fault distance data are obtained. The improved Moore voting algorithm is used to process these fault distance values so that the accurate fault distance values are determined. Finally, simulation tests verify the reliability and accuracy of the proposed scheme. The proposed scheme requires a low sampling frequency, and it can not be interfered with by the fault transition resistance and occasional large errors. These advantages help to improve the efficiency and accuracy of troubleshooting.

Fault Location and Phase Selection Method of Distribution Network Containing IIDG Based on Online Active Detection

This paper proposes a fault location and phase selection method for the distribution network containing IIDG based on online active detection, utilizing online active detection. In this method, the characteristic harmonic current injection control strategy based on d- and q-axis control is obtained by analyzing the structure, control strategy, and short-circuit current of IIDG. During a fault, the distributed power supply injects characteristic harmonic current into line, adapting its magnitude with the short-circuit current’s size. By detecting the difference in harmonic current content rate for both ends of the fault point, the method achieves online location and line selection of fault points. A 10 kV IIDG distribution network model is simulated by using PSCAD to validate the feasibility and accuracy of the proposed fault location and phase selection method.

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.

Retracted: Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Retracted: Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

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.

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Two-stage fault section location for distribution networks based on compressed sensing with estimated voltage measurements

Methods for fault location in distribution networks based on compressed sensing (CS) have been investigated and the results have demonstrated some degree of feasibility over the past years. However, in the case of a fault occurring close to a line’s terminal, the existing methods typically cannot locate the fault section correctly but just locate the fault to the bus near the fault point or adjacent section rather than the fault section. To improve the accuracy of fault location, a two-stage fault section location method is proposed in this paper. In the first stage, the CS-based model is solved by using all the measured voltages, and the fault candidate sections are identified. In the second stage, the node impedance matrix of the subnetwork formed by the fault candidate sections are calculated, and the voltage amplitudes of the buses around this subnetwork are estimated, which are then used to solve the CS-based model to finally determine fault section. Validations are implemented on a modified IEEE 33 bus system and a modified 134-bus system. The results verify a satisfactory performance in fault section location under various fault conditions.

Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm

Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that the optimized BPNN has high efficiency and accuracy.

Analysis of distribution network planning with distributed power supply based on ELM algorithm

Abstract With the development of the social economy, the importance of energy and environmental issues is becoming more and more prominent. Distributed power sources (DG) have the advantages of low carbon and environmental protection, flexible control, and low investment cost, but their nodes in the grid are random, which will increase the complexity and uncertainty of grid operation after grid connection. In this paper, we study the planning path of DGs in distribution networks and the protection technologies involved. Firstly, we focus on the impact of DG access on the protection of distribution networks and the impact of different access capacities and access locations of DG on the protection of distribution networks. Second, after studying the current status of the application of traditional genetic algorithms in distribution network fault location, a two-layer planning model based on the modified extreme learning machine algorithm (MPSO-ELM) is proposed, with the load demand guaranteed by the first layer of power allocation and the fault location incorporated into the distribution network protection scheme in the second layer. Finally, simulations are performed to compare and demonstrate the advantages of this MPSO-ELM algorithm. The simulation results show that the MPSOELM algorithm converges faster than the traditional genetic algorithm by 7s, the average operating speed of power nodes is 3.61ms faster than the traditional genetic algorithm by 5.59ms, and the accuracy in fault location is improved by 23.14%. The simulation results verify the comprehensiveness, accuracy, and high efficiency of the ELM algorithm for double-layer planning of distribution networks containing distributed power supplies, which is important for improving the level of power grid construction and avoiding the risk of power grid construction.

Modal Transformation based Fault Location in Radial Distribution Network

This paper introduces the technique of fault distance estimation based on modal transformation and signal processing. The recorded faulted phase currents are applied to the Karrenbauer model transformation and these model component currents are decomposed into detail coefficients by the use of Daubechies wavelet, db6. The fault recorder installed at the terminal of the feeder records different time delays between the modal components. In order to find fault distance, the time delay values and modal components velocity are used in traveling wave theory. This paper compares two different conditions: the first condition does not use a modal transformation and the second condition uses a modal transformation. When using modal transformation conditions, three different coefficient levels (detail coefficient level 1 (D1); the combination of detail coefficient level 1+2 (D1+2) and the combination of detail coefficient level 1+2+3 (D1+2+3) ) are used to estimate the fault distance. Different fault types with different fault locations are created in MATLAB simulation.

Grounding fault location method of overhead line based on dual-axis magnetic field trajectory

To address the challenges in fault location in distribution networks, the distribution of magnetic field under overhead line and its relationship with three-phase currents are explored in this paper. At the same time, considering the influence of sensor installation position, line sag and galloping on magnetic field, a grounding fault location method of an overhead line based on dual-axis magnetic field trajectory is proposed. The analytical expressions of the magnetic field on the x-axis and y-axis under the overhead line are obtained by least squares fitting. The Lissajous figure synthesized by dual-axis is then compared with the general equation of an ellipse, and the characteristic quantity expression characterizing the magnetic field trajectory structure is obtained. Finally, a fault location criterion is constructed using the difference of the characteristic quantities of the ellipses synthesized by x-axis and y-axis magnetic fields upstream and downstream of the fault point, i.e., the difference of the length of the major axis and the minor axis, and the sign for the ratio of the cosine value of the inclination angle. Compared with other location methods based on electrical quantity, the principle of this method is simpler and it can locate faults more quickly and accurately. A large number of simulation results show that the proposed method is suitable for different types of fault conditions.