The Research of Hybrid Genetic – Particle Swarm Optimization Algorithm Based Reconfiguration of the Electric Distribution Network

2014 ◽  
Vol 989-994 ◽  
pp. 1204-1207
Author(s):  
Xin Nan Zhou ◽  
De Ping Ke ◽  
Yuan Zhang Sun ◽  
Lu Yang Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Distribution Network ◽  
Particle Swarm ◽  
Recovery Strategy ◽  
Swarm Optimization ◽  
Electric Distribution Network ◽  
Electric Distribution

The fault-diagnosis and recovery strategy of the electric distribution network were discussed. The procedure of the hybrid genetic – particle swarm optimization algorithm, together with a practical example, was also introduced.

Reactive Power Compensation of Distribution Network Based on Immune and Particle Swarm Optimization Algorithm

2012 ◽  
Vol 229-231 ◽  
pp. 1030-1033
Author(s):  
Wei Cui ◽  
Lin Chuan Li ◽  
Lei Zhang ◽  
Qian Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Distribution Network ◽  
Particle Swarm ◽  
Reactive Power Compensation ◽  
Swarm Optimization ◽  
Operation Costs ◽  
Sweep Algorithm

The reactive power compensation optimization in distribution network has the important meaning in maintaining system voltage stability, decreasing network loss and reducing operation costs. In order to meet factual conditions, we assume the system operates in minimum, normal and maximum three load modes and the objective function of problem includes the costs of power loss and the dynamic reactive power compensation devices allocated. In this paper we use Artificial Immune Algorithm(AIA) and Particle Swarm Optimization Algorithm(PSO) to determine compensate nodes and use the back/forward sweep algorithm calculate load flows. After applied into 28-nodes system, the result demonstrates the method is feasible and effective.

scholarly journals An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaofeng Lv ◽  
Deyun Zhou ◽  
Ling Ma ◽  
Yuyuan Zhang ◽  
Yongchuan Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis

The fault rate in equipment increases significantly along with the service life of the equipment, especially for multiple fault. Typically, the Bayesian theory is used to construct the model of faults, and intelligent algorithm is used to solve the model. Lagrangian relaxation algorithm can be adopted to solve multiple fault diagnosis models. But the mathematical derivation process may be complex, while the updating method for Lagrangian multiplier is limited and it may fall into a local optimal solution. The particle swarm optimization (PSO) algorithm is a global search algorithm. In this paper, an improved Lagrange-particle swarm optimization algorithm is proposed. The updating of the Lagrangian multipliers is with the PSO algorithm for global searching. The difference between the upper and lower bounds is proposed to construct the fitness function of PSO. The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. Experiment on a case study of sensor data-based multiple fault diagnosis verifies the effectiveness and robustness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document