Reactive power optimization of generators by using particle swarm algorithm

2014 ELEKTRO ◽  
2014 ◽  
Author(s):  
Daniel Hropko ◽  
Marek Hoger ◽  
Marek Roch ◽  
Juraj Altus
2015 ◽  
Vol 740 ◽  
pp. 401-404
Author(s):  
Yun Zhi Li ◽  
Quan Yuan ◽  
Yang Zhao ◽  
Qian Hui Gang

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.


2014 ◽  
Vol 543-547 ◽  
pp. 668-672
Author(s):  
Han Ping Zhang

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.


2021 ◽  
Vol 2136 (1) ◽  
pp. 012045
Author(s):  
Kang Li ◽  
Guige Gao

Abstract Artificial intelligence algorithms are widely used to optimize problems in power systems, and reactive power optimization in power systems has achieved good results in particle swarm optimization, but there are also problems. This paper optimizes the particle swarm algorithm. The particle swarm algorithm is improved mainly by increasing the inertia weight and improving the convergence parameters. This algorithm overcomes the blindness of local optimization solution and particle swarm algorithm, and improves the calculation speed. At the same time, MATLAB is used to compile the calculation program, and the simulation results are used to verify the feasibility of the reactive power optimization algorithm used in the research of power system.


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