Papers by Keyword: Network Reconfiguration

Abstract: —Network reconfiguration is a process of changing the original structure of the distribution network system with the intention of balancing the load in every system’s feeder at the same time to optimize the operation of the system. The process involve the changing of switching state (tie switches and sectionalize switches), with the aim to find the best combination that can increase the performance of the system while satisfying with the operational constraints. The extreme necessity to the process has become a challenging mission for the researcher to overcome the reconfiguration problems. Recent years have seen a rapid development of evolutionary algorithms and swarm intelligence based algorithms to resolve for network reconfiguration problems. For that reason, this report deals with Artificial Bee Colony (ABC) algorithm to be implemented in network reconfiguration procedure to achieve the optimum level of operation. The ease and simplicity of the algorithm and the capability to find the global optimization solution has made this algorithm appropriate for this project. The objective of this work focused on improvements of distribution power system, in terms of minimizing the total real power loss and improving the voltage profile within the acceptable value. The algorithm was tested on two different radial distribution system (33 bus and 69 bus radial distribution systems)

Abstract: This paper presents a new method to determine the best configuration for a distribution system for a day considering Photovoltaic (PV) generation and load profile. In the first part, the hourly optimal configuration for a day is obtained by using Imperialist Competitive Algorithm (ICA) and in second part; a selective approach based on minimum total daily power loss is used to select the optimal daily configuration. The proposed method is validated on IEEE 33 bus test system.

Abstract: Network reconfiguration is of theoretical and practical significance to guarantee safe and economical operation of distribution system. In this paper, based on all spanning trees of undirected graph, a novel genetic algorithm for electric distribution network reconfiguration is proposed. Above all, all spanning trees of simplified graph of distribution network are found. Tie branches are obtained with spanning tree subtracted from simplified graph. There is one and only one switch open on each tie branch. Decimal identity number of open switch on each tie branch is taken as the optimization variable. Therefore, length of chromosome is very short. Each spanning tree corresponds to one subpopulation. Gene operations of each subpopulation are implemented with parallel computing method. Individuals of offspring after gene operation automatically meet with radial and connected constraints for distribution network operation. Disadvantages of conventional genetic algorithm for network reconfiguration that a large amount of unfeasible solutions are created after crossover and mutation, which result in very low searching efficiency, are completely overcome. High calculation speed and superior capability of the proposed method are validated by two test cases.

Abstract: Distribution network can operate with electricity for 1~2 hours after single-phase to-earth fault happening, so its network reconfiguration is different from not only reconfiguration in normal situation but also reconfiguration in fault situation and it should consider more factors. This paper puts forward a kind of improved branch exchange algorithm which all power supply restoring areas are divided into two groups: group A which are branch circuits on the same bus of fault line and group B which are power supply restoring lines on the different bus of fault line. For all power supply restoring lines of two groups, when voltage satisfies the constraint conditions, dispatcher prior chooses power supply restoring line which has minimum network loss value in group A in order to avoid outage during process of power supply restoring. Actual flied operation shows that this measure satisfies practical requirements.

Abstract: This paper puts forward a distribution network reconfiguration method which takes into account voltage sag, and this method takes advantage of genetic algorithm in handling optimization problems and can optimize a superior distribution operation mode which makes the important load sag in the lowest degree besides satisfies the distribution network constraint. IEEE 16 nodes system is cited as an example. The simulation result shows that the distribution network operation mode got by the proposed method ensures the distribution network loss within reasonable limits as well as minimizes voltage sag in the important load node. This approach presented is of both efficient and practical.

Abstract: The Multi-objective Optimal AlgorithmNSGA-II for Network reconfiguration of the shipboard power system is proposed to overcome the shortcomings of single objective optimal algorithm. This paper chooses the least of lost loads and cost of switch operation as objective function, uses capacity and structure as constraints, codes two-dimensional gene with switch and uses NSGA-II to solve the multi-objective and multi-restriction network reconfiguration problem. The test results of a typical integrated power system show that the model can balance each objective to avoid extreme results, which make restoration schemes more practical.

Abstract: This paper presents an improved algorithm for distribution network reconfiguration. The objectives is to minimized the power loss and the percentage of over-voltage. Based on the traditional genetic algorithm, the adaptable function selection and the disposal of terminating evolution criteria has been improved, to improve the convergence of the system and the calculation accuracy. At the same time, using a new estimation method to correct the load curve. This approach takes full advantage of existing distribution network's original data, it can significantly reduce the computation time, its accuracy to meet the requirements of engineering practice. Test results have been presented along with the discussion of the algorithm.

Abstract: Recently more and more distributed generations(DG) are accessed in distribution system. So we analyze how DG influence the distribution system. In order to make the result of network reconfiguration more closely to the reality, we build a network configuration model for distribution system with DG which contains three load modes, and we bring forth a composite algorithm which contains improved binary particle swarm optimization based on loop circuit and genetic algorithm, and combine with the taboo algorithm to avoid circuitous search. This composite algorithm can not only increase the search speed, but also effectively overcome the precocity. The simulation results show that after the network reconfiguration, the network loss has been greatly reduced, and this verifies the effectiveness of this algorithm.

Abstract: The paper proposes a self-learning evolutionary multi-agent system for distribution network reconfiguration. The network reconfiguration is modeled as a multi-objective combinational optimization. An autonomous agent-entity cognizes the physical aspects as operational states of the local substation, the agent-entities establish relationship network based on the interactions to provide service. Multiple objectives are considered for load balancing among the feeders, minimum deviation of the nodes voltage, minimize the power loss and branch current constraint violation. These objectives are modeled with fuzzy sets to evaluate their imprecise nature and one can provide the anticipated value of each objective. The method completes the network reconfiguration based on the negotiation of autonomous agent-entities. Simulation results demonstrated that the proposed method is effective in improving performance.