Reactive Power Optimization to Distribution Network with Wind Farm Based on Novel Particle Swarm Algorithm and Scene Classification

2013 ◽  
Vol 5 (8) ◽  
pp. 247-253
Author(s):  
Xinrong Liu ◽  
Guang Yang ◽  
Xuehui Zhang ◽  
Yuru Liu
Keyword(s):  
Wind Farm ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Scene Classification ◽  
Swarm Algorithm

Reactive Power Optimization for Power System with Wind Farm Based on Improved Particle Swarm Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 668-672
Author(s):  
Han Ping Zhang
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Optimization ◽  
Mathematical Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Multiple Variables ◽  
Swarm Algorithm

The problem of power system reactive power optimization is a mathematical problem with multiple variables and constraints, which is complex and non-linear. The control variables include continuous variables and discrete variables; its difficult to get the optimal solution. A solution in power flow calculation is put forward after the mathematical model of wind farm is built in this paper. Based on this, taking the WSCC 9 nodes system as an example, use the particle swarm algorithm to solve the reactive power optimization problem. The result shows that this algorithm has an apparent positive effect on reducing system power loss and improving system voltages.

A Novel Reactive Power Compensation Approach Based on Particle Swarm Optimization

2015 ◽  
Vol 740 ◽  
pp. 401-404
Author(s):  
Yun Zhi Li ◽  
Quan Yuan ◽  
Yang Zhao ◽  
Qian Hui Gang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Swarm Algorithm

The particle swarm optimization (PSO) algorithm as a stochastic search algorithm for solving reactive power optimization problem. The PSO algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model, a method using improved particle swarm algorithm for reactive power optimization problem, the algorithm weighting coefficients and inactive particles are two aspects to improve. Meanwhile segmented approach to particle swarm algorithm improved effectively address the shortcomings evolution into local optimum and search accuracy is poor, in order to determine the optimal reactive power optimization program.

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