Fault section locating for distribution network with DG based on improved ant colony algorithm

Although good application effect of locating faulty section in complex distribution network can be attained by applying ant colony algorithm, however there are also disadvantages such as too long search time and computation time. For this reason, traditional ant colony algorithm should be improved. Firstly, a switching function that can dynamically adapt to topological structure of distribution network is constructed; then a method to set the initial values of ant colony's pheromone and the disturbance rule of the solution are led in; finally the method to apply the improved ant colony in fault section location of distribution network with multi power supplies is determined. The advantages of the improved ant colony algorithm in calculation speed and error tolerance are verified by results of calculation example.

Protection coordination of distribution network has been one of the issues with the distributed generators integration. The reason is that the distribution network is radial in nature by which energy flow is unidirectional. The research aim is to investigate how the location or placement of DG affects the protection setting of the existing network. The healthiness of the test distribution system used in this work is investigated with and without DG connection by use of load flow analysis using Neplan software. The results confirm that the location or placement of DG with the distribution system connection improves the voltage profiles at each bus and active power load loss. The DG placed 1km away from point of common coupling (PCC), the voltage profiles is 10.448Kv with DG connection, and 10.442kV without DG connection, while active power load loss is 69kW with DG connection and 71kW without DG connection. Also, DG placed 6km away from point of common coupling the voltage profiles is 10. 404kV with DG connection 10.372Kv without DG connection, while the active power load loss is 110kW with DG connection and 131kW without DG is connected. Moreover, with the DG placed 1km away from the point of common coupling (PCC) the fault current 17.596kA with DG connection and 3.286kA without DG connection, while 6km away from the PCC is 17.543kA with DG connection and 3.286kA without DG connection. It can be concluded that as the distance of the DG from the PCC increases, the fault current also increases which affects the protection scheme of the existing distribution system.

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.

A distribution network plays an extremely important role in the safe and efficient operation of a power grid. As the core part of a power grid’s operation, a distribution network will have a significant impact on the safety and reliability of residential electricity consumption. it is necessary to actively plan and modify the distribution network’s structure in the power grid, improve the quality of the distribution network, and optimize the planning of the distribution network, so that the network can be fully utilized to meet the needs of electricity consumption. In this paper, a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm. For the distribution network structure planning of dual power sources, the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies, and the dual power distribution network structure model is established based on the principle of the lowest cost. The artificial ants in the algorithm were compared with real ants in nature, and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem (TSP). Then, the limitations of the ant colony algorithm were analyzed, and an improvement strategy was proposed by using python for digital simulation. The results demonstrated the reliability of model-building and algorithm improvement.

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

Reactive power optimization of distribution network is an effective means to ensure the safe and economic operation of distribution network, and it is an important measure to improve the voltage quality of distribution network. The reactive power optimization of distribution network can improve the voltage distribution of distribution network system, improve the voltage quality of users, reduce the power loss of power transmission, reduce the power cost, and improve the power transmission and distribution capacity and stable operation level of distribution network. This paper studies the optimality of ant colony algorithm and artificial bee colony algorithm for distribution network reactive power optimization. Taking the minimum system active power loss as the objective function, the mathematical model of distribution network reactive power optimization is established. Based on IEEE33 node system, the simulation experiments of distribution network reactive power optimization based on ant colony algorithm and artificial bee colony algorithm are carried out respectively, and the experimental results are compared and analyzed. The experimental results show that both algorithms can improve the system node voltage, the artificial bee colony algorithm has better performance in reducing the efficiency of system active power loss, and the ant colony algorithm runs faster.

Distribution network reconfiguration is a very complex and large-scale combinatorial optimization problem. In network reconfiguration, whether an effective solution can be obtained is a key issue. Aiming at the problems in network reconstruction by traditional algorithm, such as long time required, more times of power flow calculation and high network loss, a network optimization design algorithm based on improved ant colony algorithm for high voltage power distribution network is proposed. After analyzing the operating characteristics of the high voltage power distribution network, the network topology of the high voltage power distribution network is described by constructing a hierarchical variable-structure distribution network model. A mathematical model of distribution network reconstruction considering the opportunity constraint with the minimum network loss as the objective function is established. The power flow distribution is calculated by using the pre-push back-generation method combined with the hierarchical structure of the distribution network. The maximum and minimum ant colony algorithm is introduced to improve the pheromone updating method of the traditional ant colony algorithm, and the search range is expanded, so that the algorithm can jump out of the local optimization trap to realize the accurate solution of the power distribution network reconstruction model. The experimental results show that compared with the current network reconstruction algorithm, the proposed algorithm requires less time for convergence, less power flow calculation, and lower network loss.

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.

The access of distribution generation(DG) presented new challenges to the operation and protection of power system with the development of the smart power distribution network. The Wiring structure changes from a single power supply into a double-Power or more power. As a result,it brings impact to distributed protection. The result shows the size of DG affects protection and makes it lose selectivity probably, DG upstream protection makes it amplify protection range, DG downstream protection makes it reduce protection range. In response to the problem, this paper proposes a new scheme, which is based on the centralized processing module on the distributed protection. Firstly, a smart unit is installed in the switch, which is only responsible for monitoring and control, merging unit and smart terminal unit function, and then sample information and DI information are sent to the distribution automation system through the fiber-optic communications. And then the automation system complete protection monitoring and control functions. Through utilizing the site-wide information collected on the distribution network to achieve network-wide protection, monitoring and control functions, it improves the reliability of the distributed protection operation. Compared with the conventional system, this scheme has more flexibility and more reliability on the coordination and the whole property. It is closer to the develop direction Of the distributed networks. easier to transform into the intelligent distributed networks.

The accuracy of traditional fault current detection methods in distribution network is low. In order to achieve accurate detection of fault current, an automatic fault current detection method based on current transformer is proposed. By describing the saturated operation state of current transformer, the current saturation characteristics of distribution network are analyzed and measured. The least square calculation method is introduced to describe the fault current signal, so as to calculate the fault current amplitude of distribution network. By drawing the equivalent circuit diagram of fault current in distribution network, the fault current range and direction in distribution network can be detected automatically. In addition, through comparative experiments, it is proved that the proposed detection method can effectively improve the detection accuracy in practical application, and show the change of fault current more accurately.

In the field of fault location in distribution networks, ant colony algorithm has a relatively good engineering effect, but it has the disadvantages of long convergence time, slow calculation speed, and easy to fall into local optimum. In view of the above problems, this paper improves on the traditional ant colony algorithm. First, the population pheromone matrix of the ant is changed to a sparse matrix, which greatly reduces the time complexity of the algorithm; secondly, the clustering algorithm is used to deeply analyze the distribution network, so that the algorithm can converge toward the optimal solution in the initial situation, and further improve the convergence rate. Finally, dynamic parameters are used to set a new initial pheromone, and a new local function is constructed to avoid falling into a local optimum. The calculation shows that the improved ant colony algorithm proposed in this paper is suitable for single-point and multi-point failures in the distribution network, and has good search speed and accuracy.

High current amounts, low voltage level and radial structure assign high percentage of active power loss in distribution system. There are many means in reducing the active power loss of distribution networks; this paper introduces an ant colony algorithm to solve the optimal network reconfiguration and capacitor placement problem for power loss reduction and voltage profile enhancement in distribution networks. The ant colony algorithm was inspired from natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trial formation. Ants of artificial colony are able to search for the successively shorter feasible routes by using information accumulated in the form of a pheromone trial deposited on the edge of their traveling path. The proposed approach is demonstrated employing one distribution network. Computational results show that by taking into account feeder reconfiguration and capacitor placement simultaneously, one can be minimize losses more efficiently by considering them separately.

As the amount of distributed generation (DG) integrated into the distribution network keeps increasing, this leads to an increase in the levels of fault current in the network, and this will result in the network having a fault current above what the existing protecting devices can handle. Hence, an upgrade of protective devices will be necessitated. A lot of techniques have been developed to mitigate against high fault currents. Some of these techniques with their limitations include current-limiting fuses that need constant replacement after an operation; circuit breakers that are very expensive for high current applications; isolation transformers that result in additional network power losses; and current-limiting and air-core reactors that impede the voltage stability of the network. Following these shortcomings, fault current limiters saw the light as the paramount solution to restrain fault currents in the distribution network. This research work is a follow-up of previous research that was on the design of a modified bridge-type nonsuperconducting fault current limiter (MBNSFCL) for application in the distribution network. In this paper, the designed MBNSFCL is tested and validated on two standard IEEE distribution networks, which are the IEEE 13 and the 33 node test networks. The simulation was done using PSCAD/EMTDC. In the case of the IEEE 13 node, the occurrence of a 3 phase to ground fault on the swing bus leads to the source current shooting from an amplitude of 0.602 kA on phase A, 0.335 kA on phase B, and 0.356 kA on phase C to 34.996 kA on phase A, 35.126 kA on phase B, and 34.983 kA on phase C. The fitting of the MBNSFCL into the test network restrains the fault current below the nominal line current, a level at which the circuit breaker can comfortably clear. The virtue of the MBNSFCL is also established when tested on the IEEE 33 node test network. This reveals the reliability of the proposed MBNSFCL for application in the distribution network.

Photovoltaic power generation, with the advantage of renewable energy and convenient inst allation, has been widely used in distributed generation in distribution network as the technology gradually developed. Grid-connected photovoltaic power generation station not only changes the struc ture single terminal power supply distribution network, affect the operation of distribution network due to its fluctuant and intermittent characteristics. As grid-connected photovoltaic power station adopts power electronic converter for the connection to distribution network, the harmonic voltage and current as well as the DC components are inevitable. Therefore, the impact analysis and the comprehensive evaluation of grid-connected photovoltaic power station are studied.The main c ontents are as follows: 1: Based on the multiple CPUs embedded measurement and evaluation system designed for the grid-connected photovoltaic power generation station, a positive and negative effect index is pr oposed for its comprehensive evaluation. With FFT as basic power measurement algorithm and a dopted DSP computing unit, data manage unit and analysis unit, a real-time calculation of positive a nd negative effect index is realized. 2: Common weights of the index are determined by DS/AHP method with incomplete information, and then the weight synthesizing of positive and negative effect index are conducted through penalty-incentive balance functions respectively. The comprehensive evaluation results are generated by the method of grey relation analysis.

Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution, to get ant colony algorithm's initial pheromone distribution and according to the pheromone content to eliminate some poor path. Secondly, using ant colony algorithm for the optimal path, it was gotten by particle swarm optimization for the secondary search. Finally, get the optimal solution of path search. The simulation results show that, the fusion algorithm does better than the ant colony algorithm in terms of path optimization.