scholarly journals Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7145
Author(s):  
Minsheng Yang ◽  
Jianqi Li ◽  
Jianying Li ◽  
Xiaofang Yuan ◽  
Jiazhu Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Network Structure ◽  
Distribution Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Fault Recovery ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Dc Distribution

DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Lévy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Lévy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. Finally, the results validate the accuracy and efficiency of the proposed method.

scholarly journals Reconfiguration of Distribution Networks with Distributed Generation Using a Dual Hybrid Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Caoyuan Ma ◽  
Chunxiao Li ◽  
Xuezi Zhang ◽  
Guoxin Li ◽  
Yonggang Han
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Distribution Network ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Optimization Search

This paper proposes a reconfiguration strategy of distribution network with distribution generation (DG) based on dual hybrid particle swarm optimization algorithm. By the network structure simplification and branches grouping, network loss was selected as objective function, an improved binary particle swarm optimization algorithm (IBPSO) was used in branch group search, and the proposed group binary particle swarm optimization search algorithm was used in searching within the group to improve search efficiency and avoid early maturing. The proposed algorithm was tested on the IEEE 33-bus distribution power system and compared with other existing literature methods. The influence on the power flow of distribution network by DG position and capacity was studied. Simulation results illustrate that the proposed algorithm can get the optimal configuration results and significantly reduce system energy losses with fast convergence rate. In order to control the smart grid, using a dual hybrid particle swarm optimization algorithm to reconstruct a model, the result of simulation verifies the validity of the model. At the same time, the distributed power grid after reconstruction after optimization can effectively reduce the network loss and improve power supply quality.

Optimization of Large Vessels Principal Parameters Based on Hybrid Particle Swarm Optimization Algorithm

2012 ◽  
Vol 253-255 ◽  
pp. 2172-2175
Author(s):  
Wen Quan Wang ◽  
Sheng Huang ◽  
Yuan Hang Hou ◽  
Yu Long Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Design ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Bee Colony ◽  
Hybrid Particle Swarm Optimization ◽  
Large Vessels

The multi-objective optimization design model of large vessels principal parameters was established, according to the characteristics of large ship’s scheme design. The ship stability, rapidity, and seakeeping were selected as the three objectives of the optimization model, and the minimum-deviation method was adopted to establish the unified objective function. The Particle Swarm Optimization and the Artificial Bee Colony algorithm were combined to the hybrid particle swarm algorithm, which then was used to solve the mathematical model. Through the simulation calculation, the results show that the hybrid algorithm has a better optimization performance and it is feasible for hybrid algorithm to apply in the preliminary design of large vessels.

Method of Testing Point Optimizing Based on Multi-Echelon Comparison Hybrid Particle Swarm Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1935-1939
Author(s):  
Zhi Qiang Wang ◽  
Yan Xu ◽  
Qing Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Simulation Analysis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Fundamental Particle ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

This paper introduces the multi-echelon comparison hybrid particle swarm optimization (PSO) algorithm and its processes, and describes its application in the testing point optimization, and the simulation analysis and comparison show that the algorithm improves the convergence speed of global search, and overcomes the shortcoming that fundamental particle swarm optimization algorithm is easy to fall into "premature" convergence and other shortcomings.

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