Research on Fault Location Method of Distribution Network Based on Archimedes Optimization Algorithm

The research on fast and accurate fault location technology in distribution networks is of great significance. Currently, many methods for fault location in distribution networks have been proposed, but most theories are difficult to solve the problem of fault location in multi branch distribution networks. Therefore, this paper analyzed the characteristics of traveling wave transmission and proposed a discrimination algorithm that uses the polarity of traveling waves to determine the source of fault traveling waves; Then, using the idea of first locating the fault section and then locating the fault point, a multi branch distribution network traveling wave fault location algorithm is proposed based on the configuration of micro PMU (Phasor Measurement Units) devices in the distribution network by combining the two end method and single end method of traveling wave distance measurement. A multi branch distribution network simulation model based on IEEE14 node standard topology was built using MATLAB/Simulink software, the experimental results also showed the effectiveness and the efficiency of the proposed algorithm.

When the distribution network fails, the operation and maintenance personnel need to quickly determine the fault location and minimize the scope of the inspection line so that the fault repair personnel can accurately reach the vicinity of the fault point to carry out work. Whether the fault location method is advanced and reliable directly affects the power failure time of the power supply user, and further affects the social, economic, and life aspects. Therefore, the fault location of the distribution network needs to satisfy both reliability and accuracy. It is the basis and premise for troubleshooting, isolating and restoring the power supply. The automation level of China’s distribution network is relatively backward, and the reliability of power supply equipment is relatively low, making it difficult to find fault points. If we find out the intelligent algorithms that can meet the requirements of rapid positioning, accuracy and fault tolerance through theoretical research, we can Improve the fault location efficiency very well.

Fault location in electrical energy distribution networks is an important task, as faults in distribution grids are among the main causes of electricity supply disruption. Fault location in the distribution systems, however, is a challenging task because of the topology of the distribution networks, as well as the main and side branches. Therefore, it is necessary to address these challenges through an intelligent approach to fault location. In this paper, fault location in electric energy distribution networks is addressed considering the changes in fault distance and fault resistance in the presence of different fault types. A new method for fault location is developed for conditions where the minimum information is available and only information at the beginning of the feeder is used. This facilitates wide adoption of the technique as it does not require significant investments in instrumentation and measurement. The proposed intelligent method is based on the impedance and transient state estimation. This technique employs a specific impedance analysis for determining possible fault locations considering the unbalanced performance of distribution systems, distances, and different fault resistances. To determine the real faulty section, real fault frequency component analysis and the simulated faults at possible fault locations are used. At this stage of the process, it is possible to eliminate multiple estimations with the help of comparison and identification of the similarities. Therefore, a real faulty section is determined. It is observed that some conditions of electric energy distribution networks affect the accuracy and performance of the proposed method significantly; thus, a detailed investigation is conducted to neutralize these conditions. Simulation results and calculations based on MATLAB along with a practical test of the proposed method in power network simulator confirm a satisfactory performance.

Although single phase to ground fault location in non-solid grounded networks has been studied for many years and a lot of research has been done, yet adequate solution for direct fault location of radial distribution network hasn't been made, especially for the distribution network with the neutral point grounded via arc suppression coil. Involvement of the arc suppression coil may decrease short-circuit current, but the difficulty of finding fault location is increased. The key problem of non-solid single-phase to ground fault location is to solve the fault location of distribution network with the neutral point grounding via arc suppression coil. This paper will analyse line capacitive current distribution of isolated neutral point and neutral point grounded via arc suppression coil and propose the concept of per-unit zero sequence current method. Equipartition principle of zero sequence current is utilized to locate fault section of the distribution system. Simulation tests to verify the effectiveness of this method have been made as well.

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario.

With the increasing attention to the power supply reliability of distribution network, the fault location method of distribution network as an important subject can be studied in depth. Traditional fault location methods, such as impedance method and traveling wave method, are limited due to the complex topology and many line types of distribution network. Based on the analysis of the limitations of impedance method and traveling wave method, this paper studies the fault location method of distribution network based on time reversal, focusing on the influence of hybrid power network and load on fault location method, Compared with the traditional fault location method, the distribution network fault location method based on time reversal has stronger adaptability.

With the rapid development of national economy, the distribution network becomes more and more complex and the load density is increasing. The safe and stable operation of distribution network is crucial to reliable power supply. The faults in distribution network account for a large proportion in the whole power system, and have an increasing trend. Therefore, accurate fault location of distribution lines is of great significance for fast power recovery. At present, the traveling wave fault location of distribution network is usually based on the fault location method for transmission lines. The engineering situation of distribution network is not fully considered and the analysis is simple, which affects the precise analysis of fault traveling wave transient characteristics and the improvement of fault location method. In this paper, the transient characteristics of fault traveling wave under different fault conditions in distribution network are analyzed and the analysis results are validated by simulation.

The addition of distributed generation sources into conventional distribution networks results in several technical challenges for successful operation of distribution network. As far as physical integration is concerned, finding the exact location of fault is a key issue. The coordination between the protection devices existing in the distribution network gets disrupted due to presence of distributed generation (DG). Therefore existing fault location techniques for distribution system will not work properly with penetration of DG. Therefore new fault location schemes for distribution networks with distributed generations (DG) have been developed in the recent times, but there are still problems whose solution needs to be found out. This paper investigates the issues and current state regarding fault location in distribution network with DG. A comparative analysis is carried out in which the merit and demerit of each technique is assessed and some conclusion and suggestion for fault location in distribution system with DG is put forward.

Fault location in distribution networks is a critical task for ensuring reliable power delivery and minimizing downtime. This paper proposes a fault location method for distribution networks based on Support Vector Machine (SVM) algorithms. The method utilizes voltage and current measurements from various network nodes to identify fault characteristics. A set of key features, including voltage sag, current surge, and their time-domain characteristics, are extracted from the data. The SVM classifier is trained on these features to differentiate between fault and normal conditions and accurately locate the fault. Experimental results using simulated data demonstrate that the proposed method significantly improves fault detection accuracy and reduces the fault location time compared to traditional techniques. The effectiveness of this method is further validated under different fault scenarios and network configurations, showing its robustness and potential for practical deployment in real-world distribution networks.

Efficient and accurate fault location techniques are beneficial to pinpoint fault position and reduce power outages. Faced with this issue, this paper proposes a novel fault location technique for active distribution networks that utilizes multiple measurement-based Bayesian learning. Specifically, fault location problem in this paper is firstly transformed into solving a block-sparse signal recovery model. In order to enhance robustness in noisy conditions and achieve satisfactory recovery performance, this paper extends the recovery model to a multiple measurement-based model and adopts a block-sparse Bayesian learning (BSBL) algorithm for block-sparse signal recovery. The proposed method requires only a limited number of distribution-level synchronized measurements to be placed, instead of yielding a full observable network. The effectiveness of the proposed method under different noise levels is verified by using IEEE 33-node active distribution system.

Distributed generation is widely used in power systems, but the topology becomes complicated after the grid connection. Because of the large-scale distributed generation grid, the difficulty of fault location is increasing, and the terminal is easily affected by environmental influences, which leads to the uploaded information being easily distorted. This paper proposes an improved algorithm to solve the defects of the standard intelligent algorithm in the location of high-dimensional and large-scale distributed grids. At the same time, a hybrid algorithm combined with the improved matrix algorithm is proposed to solve the location of the fault section. The experimental analysis proves that the location method studied in this paper can guarantee the distribution network’s security and stability and the power supply’s reliability. The experimental analysis has significant engineering value in the distribution network fault location.

Aiming at the problem that the traditional method is difficult to satisfy the fault location of distribution network with charging load, a distribution network fault location method based on graph theory and deep learning under charging load access is proposed. Firstly, the graph theory is introduced to decompose the distribution network topology into several Y-shaped tree structures, and the distribution network topology model is constructed; Then, the fault current characteristics of distribution network during electric vehicle reverse discharge are analyzed. The mapping relationship between response data and fault location is mined by using generalized network learning (GDL), and the fault feature training model is constructed; Finally, graph theory and GDL is used for fine fault location. The experimental results show that the proposed method for short circuit fault are 98.09% and 0.019 km respectively. This method can effectively improve the positioning accuracy, accurately determine the fault section, and has strong fault tolerance.

The 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 of fault location of distribution network based on the tree structure diagram and improved Dempster-Shafer Theory of evidence is proposed to solve the problem of misjudgment caused by abnormal fault information in distribution network. Firstly, this paper presents a new search method based on tree structure diagram. Through the tree structure diagram, we can ensure the initial positioning result respectively based on three different alarm information. The three alarm information include fault indicator information, distribution transformer information and trouble calls. And then, through using the improved Dempster-Shafer Theory of evidence, we can integrate the initial positioning results to get the final positioning result. Finally, a concrete example is given to demonstrate the effectiveness and feasibility of this method, which indicates that the method can solve the problem when misinformation or information happens. The outstanding advantage of this method is that the multi-source information is used to locate the fault, and the misjudgment due to the abnormality of the single source information can be avoided.

With the access of distributed generators (DG), the power flow direction and structure of distribution networks have changed, and therefore many traditional fault location methods are no longer applicable. Single-phase grounding fault is a common fault in power distribution network, which may bring secondary fault and even blackout. Starting from the relationship between the wavelet eigenvalues of traveling waves and the fault section of distribution lines with DG, the optimal fault features were selected by dimension reduction of linear discriminant analysis (LDA), and then the Bayesian classification model based on kernel distribution was used to realize the new method of Single phase grounding fault location. The IEEE 33-bus model with DGs was constructed to test the faults in different sections of active distribution network, the locating accuracy of the optimal three-dimensional feature sample is 97.9%. The result shows the method can achieve accurate fault location.