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.

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.

Reactive Power Optimization in Power System Based on Adaptive Particle Swarm Optimization

2013 ◽  
Vol 846-847 ◽  
pp. 1209-1212
Author(s):  
Wen Qing Zhao ◽  
Li Wei Wang ◽  
Fei Fei Han ◽  
De Wen Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Reactive Power Optimization ◽  
Algorithm Optimization ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
System Characteristics

This paper summarizes the reactive power optimization of power system characteristics and requirements, proposed to target the active power loss of reactive power optimization mathematical model, And the traditional classical algorithm can not handle the limitations of discrete variables, using the adaptive particle swarm optimization algorithm to solve the problem of reactive power optimization. By testing on IEEE30 bus system simulation, comparing different algorithm optimization results show the effectiveness and superiority of APSO algorithm.

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