A fault location method for distribution network feeder terminal based on SABSO algorithm

With the increasingly prominent problem of faults at the end of distribution network feeders, a fault area localization method based on simulated annealing Tianniu swarm optimization (SABSO) algorithm is proposed to address the shortcomings of traditional fault localization methods. This method achieves rapid and accurate location of fault areas by collecting and analyzing fault information at the end of the distribution network feeder. The experimental results show that this method has higher accuracy and can accurately locate the fault area at the end of the distribution network feeder, providing strong support for the safe and stable operation of the distribution network.


Introduction
The Beetle Swarm Optimization (BSO) algorithm is an intelligent algorithm used for global optimization, combining the beetle foraging mechanism and swarm optimization algorithm [1][2].It has good robustness and running speed.This article draws inspiration from this algorithm and combines the Beetle Swarm Optimization algorithm with the Particle Swarm Optimization algorithm [3].The Tenebris algorithm is an intelligent algorithm used for global optimization, originating from the process of Tenebris searching for food.The Tenebris algorithm simulates the process of a Tenebris searching for food, where the left and right tendrils of the Tenebris can sense the odor concentration emitted by the food [4].The Tenebris determines the direction of the next move based on the difference in concentration perceived by the left and right tendrils until the food is found.The Tianniuxu algorithm does not require specific formulas or gradient information to achieve efficient global optimization.After collectivization, information can be shared among different longicorn beetles, accelerating convergence speed.The basic idea of the Simulated Annealing (SA) algorithm comes from the physical annealing process, which mainly consists of the following components, the heating process, the isothermal process, and the cooling process [5][6][7][8].The combination of the Tianniu whisker algorithm and particle swarm optimization algorithm only makes the Tianniu swarm more organized, achieves information sharing, increases its search efficiency, and can more quickly find the optimal target, but to some extent, it still cannot avoid falling into local optima.On this basis, the simulated annealing algorithm is introduced to construct the Simulated Annealing Beetle Swarm Optimization (SABSO) algorithm, which can avoid falling into local optima.The SABSO algorithm has good convergence performance and search efficiency and can obtain optimal solutions to practical problems.
The terminal fault of the distribution network feeder refers to the fault that occurs in the last section of the distribution network feeder, usually in the line section between the distribution transformer and the user.This type of fault may involve situations such as short circuits, open circuits, and ground faults.The fault at the end of the distribution network feeder may be caused by various reasons, including equipment aging, equipment failure, external force damage, etc. [9].When a fault occurs, the users connected to the end of the feeder line will be unable to supply power normally, resulting in power supply interruption or instability.The location of the fault area at the end of the feeder line in the distribution network refers to the determination of the specific fault location through a series of technical means when a fault occurs at the end of the feeder line.Accurate fault location can help operation and maintenance personnel quickly locate the fault area, shorten the recovery time of the power supply, and improve the reliability and operational efficiency of the power grid [10].
Significant achievements have been made both domestically and internationally in the research of fault location at the end of feeder lines in distribution networks.Domestic research mainly focuses on intelligent sensors and power characteristic parameters, achieving accurate fault localization through data analysis and algorithm models.Foreign research focuses on fault location indicators, line parameter estimation, and graph theory optimization methods, providing fast and accurate fault area localization solutions.These achievements provide reliable technical support for distribution network operation and maintenance personnel, improve fault location accuracy, reduce power outage time, and provide strong guarantees for the stability and efficiency of power operation.In actual operating environments, the distribution network is complex and variable, and various factors can affect the accuracy and reliability of fault location, such as load fluctuations and line impedance changes.Therefore, it is necessary to further strengthen the research and verification of fault location methods in actual operating environments.
The SABSO algorithm-based fault location method for distribution network feeder terminals is a method that uses swarm intelligence optimization algorithms to solve distribution network fault problems.Through the searchability of swarm intelligence optimization algorithms, it is possible to quickly and accurately locate fault areas in the distribution network, improve fault detection and maintenance efficiency, and ensure the stable operation of the power grid.

Encoding of fault information and determination of objective function for distribution network feeder terminal
After a fault occurs at the end of the distribution network feeder, we collect the fault current information uploaded by the FTU installed at the switch at the end of the distribution network feeder when the fault occurs and perform binary encoding.Using "1" and "-1" to represent fault current information, it is assumed that the direction of flow from the system power supply to each section is positive, and it can be concluded that: where ( 1, 2,..., ) is the code for the i nd switch state.The switch function is used to determine the specific location of the fault at the end of the feeder line in the distribution network.The switch function is: ] ( ) where  represents a logical operation; M and N are the numbers of line sections in the upstream and downstream distribution networks, respectively, with the switch number i as the boundary point; represents the status values of the feeder terminals in the upstream and downstream distribution networks, with the switch number i as the dividing point.

Solving model based on SABSO algorithm
The Beetle Antennae search algorithm (BAS) is a heuristic search algorithm inspired by the foraging behavior of longicorn beetles.The whiskers of longicorn beetles are randomly explored in space.
When food is found, they are nourished and grow; when food is not found, they gradually shrink.This process can be likened to a search process where the searcher needs to find the optimal solution among possible targets.Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm that simulates the social of animal populations such as schools of birds and fish.In PSO, each particle saves its historical optimal position and global optimal position.Through particle collaboration and information sharing, the optimal solution to the problem can be more effectively found.However, whether it is the Tianniuxu algorithm or the particle swarm optimization algorithm, they may fall into local optima.The simulated annealing algorithm provides an effective method to avoid this situation.The simulated annealing algorithm is based on the principle of the solid-state annealing process, treating the search process as a Markov process.During the annealing process, the system gradually reduces its energy over time, allowing it to escape from local optima and search for new ones.
The Tianniu swarm algorithm based on simulated annealing can be implemented as follows: (1) Initialization: We initialize the position and speed of the longicorn population, as well as the initial temperature, cooling factor, and other parameters of the simulated annealing algorithm, to obtain the individual optimal solution and the global optimal solution.After comparing the two optimal solutions, we retain the more optimal solution.At this point, the initial temperature determined is: where best g represents the optimal population value of the Tianniu Colony Algorithm, and () f represents the fitness function of the Tianniu Colony Algorithm.
(2) Evaluation: We calculate the fitness value of the position (solution) of each longicorn at the current temperature.0 ) where best p represents the individual optimal value of the Tianniu Swarm Algorithm.
(3) Selection: Based on the fitness value, we choose the current optimal longicorn according to the following formula.

()
where i x represents the position vector of the Tianniu Swarm Algorithm.
(4) Judgment: If the stop condition is reached (such as reaching the preset maximum number of iterations, or the quality of the solution meets the requirements), we will stop searching and output the optimal solution; Otherwise, we will go back to Step 2. This combined method can utilize the global search ability of the Tenebrio algorithm and the group collaboration ability of the particle optimization algorithm, while avoiding falling into local optima through the simulated annealing algorithm, thereby improving search efficiency and quality.The specific flow of the algorithm is shown in Figure 1.

Experimental results and analysis
We simulate the operation status of a household distribution network in a simulation environment, which includes an 11 kV substation, a feeder, multiple segmented switches, and fault simulation equipment.The length of the feeder is 5 km, with a total of 10 segmented switches, each of which controls a 500 m long section of the line.There are 40 load nodes connected to the end of the feeder line, and each node is equipped with a smart electricity meter.The terminal voltage of the feeder is BSO algorithm have the highest F1 value, proving that the accuracy of the fault area results of the method in this article is the best.

Conclusion
This article proposes a method for locating fault areas at the end of feeder lines in distribution networks based on the SABSO algorithm.This method achieves precise positioning and elimination of fault areas by collecting and analyzing fault information at the end of feeder lines in distribution networks.The experimental results show that this method can be effectively applied to fault locations at the end of feeder lines in distribution networks.Compared with traditional fault location methods, this method has higher accuracy and can quickly and accurately locate the fault area at the end of feeder lines in distribution networks, enhancing the effectiveness of fault handling.This provides a new solution for the safe and stable operation of the distribution network.

1 tQ
represents whether the end of the feeder line in the upstream distribution area is connected to the distribution network; 2 t Q represents the switching coefficient of whether the end of the feeder line in ICEEPS-2023 Journal of Physics: Conference Series 2728 (2024) 012068IOP Publishing doi:10.1088/1742-6596/2728/1/0120683 the downstream distribution area is connected to the distribution network; "0" represents no connection and "1" represents connection; is the status value of all line sections that pass through the path from the switch with the number i to the end of the feeder line in the upstream distribution area and the end of the feeder line in the downstream distribution area;

StartYFigure 1 .
Figure 1.Flow chart of fault location method for feeder terminal of distribution network.