Improved Particle Swarm Algorithm in the power system reactive power optimization

2014 ◽  
pp. 1255-1258
Author(s):  
Kai Wang
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
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.

scholarly journals Research on Reactive Power Optimization of Power System Based on Improved Particle Swarm Algorith

2021 ◽  
Vol 2136 (1) ◽  
pp. 012045
Author(s):  
Kang Li ◽  
Guige Gao
Keyword(s):  
Power Systems ◽  
Power System ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Swarm Algorithm

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.

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.

Research for Wind Power System Reactive Power Optimization Based on Improved Artificial Fish Swarm Algorithm

2014 ◽  
Vol 596 ◽  
pp. 241-244 ◽  
Author(s):  
Shun Hua Zhang
Keyword(s):  
Nonlinear Programming ◽  
Power System ◽  
Wind Power ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Wind Power System ◽  
Swarm Algorithm

Wind power system reactive power optimization problem is a very complicated nonlinear programming problem, using the traditional reactive power optimization algorithm such as nonlinear programming method, the genetic method, particle swarm algorithm, etc; it's easy to have a slow convergence speed, into a local optimal solution of problem. To solve these problems, we improve algorithm accordingly. According to the actual situation of wind power system, adjust the parameters, especially the number of artificial fish and vision for the dynamic testing, the selection, crossover and mutation in the number of artificial fish for debugging, in order to achieve satisfactory effect.

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