Research on Fault Location of Low Voltage Distribution Network Based on Improved Ant Colony 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.

Ant colony algorithm is a heuristic search algorithm based on population optimization, which is derived from the foraging behavior of real ants in nature. It has the characteristics of positive feedback, fault tolerance and distributed collaborative optimization mechanism, but the ant colony algorithm is easy to fall into local optimum and slow to search. Based on the traditional ant algorithm, this paper applies the improved ant colony algorithm to the fault location of distribution network, establishes the switching function, and divides the distribution network, simplifying the dimension of feasible solution of ant colony algorithm. The simulation results show that the improved ant colony algorithm is effective in the location of the distribution network segment in the case of information distortion and loss.

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.

Track planning for drones has been a common problem. In order to ensure that the UAV can fly long distance in accordance with the predetermined path, it is necessary to set calibration points on the flight path to correct the sensor errors of the UAV. This problem can be abstracted into a path planning problem and solved by ant colony algorithm. Considering the uncertainty of correction failure in these calibration points. This paper presents an improved ant colony algorithm and set up the “Enhanced pheromone volatilization strategy“ to ensure that the UAV could reach the destination with the greatest possibility in this uncertain situation. We verify our algorithm on public data sets**. On data set 1, our algorithm has a 100% probability of reaching the destination, while the traditional ant colony algorithm has only a 61% probability of reaching the destination. On data set 2, our algorithm has a 56% probability of reaching the destination, while the traditional ant colony algorithm cannot find a path can reach the destination. The algorithm code*** in this paper is simple to implement, strong robustness, and can be extended to other scenarios.

Using the new method and roulette wheel, the traditional Ant Colony Algorithm is improved in the aspect of searching process and pheromone modification. The defects of low searching efficiency and easy to fall into local minimum in traditional Ant Colony Algorithm are remedied. the validity of the improved algorithm has been verified using a testing function.Moreover,a satisfactory optimum solution for a Power Distribution Network Planning has been obtained. And the effective design method for permanent-magnet synchronous Ant colony algorithm is a simulation based on the species evolution to solve complex optimization problem for heuristic. The idea of ant colony algorithm are simulated ants foraging behavior,That is, the use of a large number of ants in the search space in the random search,And use of information has always been to strengthen the search route, and guide other artificial ants search,At the same time, the introduction of the volatile pheromone mechanism(1). Ant colony algorithm proposed by the Italian scholars Dorigo in the twentieth century nineties. The algorithm in the traveling salesman problem (TSP), quadratic assignment problem (QAP) and shop scheduling problem (JSP) solution to achieve good results. Now ant colony algorithm has been optimized in the motor design, network distribution, function optimization and integrated circuit wiring in areas such as applied. Forestry waste from electricity distribution network from the power station involved in each location, size to meet future demand for electricity in rural areas since, at the same time for each subject since the power station capacity, radial network structure, as well as reliability requirements, such as binding. Because of lot of variables and constraints involved, spontaneous power distribution network planning is a very complex combinatorial optimization problem. In this paper, these issues of improved ant colony algorithm and proposes a multi-modal adaptive ant colony pheromone search mechanisms, and their use for the electricity distribution network optimization, and achieved good results. I. THE MATHEMATICAL MODEL OF ACA The ant optimization algorithm is mainly composed of the switching rules and the renewaling information element rule. As an example,We present the ACO algorithm applied to the TSP(Traveling Salesman Problem) for illustrating the principle of ant colony algorithm. there is the assumption N cities, traveling salesman problem are looking for an optimal travel path of the shortest route. The TSP models the situation of a travelling sales man who is required to pass through a number of cities(2). The goal of the travelling sales man is to traverse these cities(visiting each city exactly once)so that the total travelling distance is minimal. Feasible solution of the traveling salesman problem is a non-repeat sequences of all the citis. Assumeing that only m ants Add to the given n cities: dij(i,j=1,2,…,n) where dij is distance between city i with city j. bi(t) where bi(t) is the number of ants is located in city i when it's t. m= ∑ = n

The low-voltage distribution networks have some structural features, such as short supply line, more lines branch and influence of transition resistance of the short-circuit current. These characteristics seriously affect the development of fault location technology. The paper, based on the basis of the structural features about the low-voltage distribution networks, create the distribution networks of description matrix by knowledge of mathematical topology and use the distribution network fault location algorithm based on web-based structure matrix. By the algorithm, the area of fault judgment and the actual distribution network fault location are fully consistent. The new theory provides a new way of thinking for fault location in low-voltage distribution networks.

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.

Because the traditional ant colony algorithm has the disadvantages of slow convergence speed and easy to fall into the local optimal solution in complex environments, so the paper improves the traditional ant colony algorithm. The improved method sets the initial pheromone concentration based on the position information of the current feasible target point and the end point, to improve the early convergence speed of the algorithm. And by optimizing the pheromone update method to speed up the global convergence speed of the algorithm and reduce the generation of local optimal solutions. Increase the convergence speed of the algorithm by increasing the transfer factor and optimizing the transfer mode. Combined with the motion characteristics of AUV, the path is re-planned to further optimize the path and improve the feasibility of the algorithm. Simulation results show that compared with the traditional ant colony algorithm, the improved algorithm conforms to the motion characteristics of AUV, has a faster convergence speed, and reduces the generation of local optimal solutions.

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.

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.

This paper proposes a fault location estimation method for distribution networks considering errors from measurement devices. The method determines the fault location based on the voltage sag and current magnitudes. The fault location is based on faulted section and fault distance measured from one end of the bus. Faulted section is determines by matching the measured voltage and current with a pre-developed data. For multiple faulted sections, ranking analysis is employed to rank the most likely faulted section. Fault distance is calculated from one end of the identified faulted section. By determining the faulted sections, a fault in homogenous and non-homogenous networks can be located. The method has been tested using an actual Malaysian 11 kV distribution network. Different values of measurement errors have been applied, which includes positive and negative values. The test results showed the effectiveness of the method under various uncertainty of error from measurement devices. The proposed method can serve as an alternative technique for estimating fault location in distribution networks.

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.

In view of the disadvantages of the basic ant colony algorithm in solving non-deterministic polynomial problems, such as slow convergence rate and local optimal solution. In this paper, the updating rules of pheromone and the updating strategy of target city in ant colony algorithm are improved, and an improved ant colony algorithm (IACA) is proposed. In addition, it is applied to the shortest traversal path search of travel salesman problem. Taking 33 provincial capitals and berlin52 problems as test objects, this paper compares the search efficiency of the proposed algorithm with that of the basic ant colony algorithm. It can be seen from the simulation results that IACA has a faster convergence rate than the basic ant colony algorithm. IACA is obviously superior to the basic ant colony algorithm in terms of convergence and the quality of the optimal solution. It provides a new choice for solving similar optimization problems.

In order to solve the problems of ant colony algorithm in the path planning of mobile robots, such as slow convergence speed and easy to fall into local optimum, an improved algorithm of adaptive ant colony algorithm is proposed in this paper. The algorithm improves the ant’s transition probability function by adding the number of revolutions and the angle of rotation to the heuristic function, and adding the target guide element, which can make the algorithm converge faster. The pheromone update formula is based on the wolfs food competition and distribution principle, which adaptively updates the pheromone volatility that satisfies the Cauchy distribution to avoid the local optimal solution found by the algorithm. Finally, by comparing and analyzing the statistical data results obtained by simulation, it can be concluded that the improved ant colony algorithm in this paper is better.

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.