Power system reactive power optimization based on improved PSO

Liu, Wei; Gao, Bingkun; Liang, Xinlan

doi:10.1109/wcica.2010.5553938

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In power system's operation, existing reactive power resources could be optimal configuration through reactive power optimization (RPO). The purpose is to minimize system active power losses, maintain acceptable voltage levels and assure the grid's security and stability [1]. In recent years, artificial intelligence, such as, genetic algorithm [2,3], particle swarm algorithm [4] and ant algorithm [5][6][7] has been formed through simulating the biological behavior of nature.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization Algorithm of Particle Swarm Optimization with Sensitivity Analysis

Liu

Jia

Wang

et al. 2013

AMR

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Reactive power optimization is very important to power systems economic operation and nowadays, the research about it gets more and more popular. The paper presents a reactive power optimization algorithm of particle swarm optimization combined with sensitivity analysis. The paper first builds the mathematic model of reactive power optimization and introduces particle swarm optimization. Then, presents the sensitivity method in detail and talks about the process of computing the sensitivity. Finally, take the algorithm into practical application and the results proves that sensitivity analysis could improve the particle swarm optimization algorithm.

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Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization Algorithm of Particle Swarm Optimization with Sensitivity Analysis

Liu

Jia

Wang

et al. 2013

AMR

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“…Since PSO is easy to understand and implement, requires little parameter tuning and exhibits robust global convergence, it has received increasing interest from the optimization community. It is applied in many fields such as fussy control [2], NN-training [3], and optimization [4], etc. However, PSO suffers from a common problem of premature convergence, especially in high dimensional functions.…”

Section: Introductionmentioning

confidence: 99%

Improved Embedded Micro-PSO for High-Dimensional Optimization Problems

Han

Zhu

2012

AMM

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This paper presents a novel optimization technique called embedded micro-particle swarm optimization (EMPSO) to solve high-dimensional problems with continuous variables. The proposed EMPSO adopts a population memory which is divided into two portions as the source of diversity, and an external memory to collect particles performing well in an embedded PSO with a very small population size. However, the fact that the new method doesn’t excel in all of the benchmark functions highlights the necessity of developing improvement. Thus an adaptive mutation operator is introduced into EMPSO to remedy the issue. The experimental results show that the improved EMPSO has good performance for solving large-scale optimization problems.

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Improved MICROPSO Algorithm and Its Application on Reactive Power Optimization

Han

Sun

2012

2012 Asia-Pacific Power and Energy Engineering Conference

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In this paper, micro-particle swarm optimizer (MICROPSO) is improved and applied on the reactive power optimization problem. Self-adapted mutation operator is introduced in MICROPSO. For self-adapted mutation operator, mutation ratio is inverse-proportional to the fitness. So particles with worse fitness have higher mutation probability, and the algorithm can evolve. The self-adapted mutation operator keeps diversity of the particles. Simulation results on reactive power optimization of IEEE 30 system show that the solution of improved micro-particle swarm optimizer (IMICROPSO) with 4 particles is even better than standard PSO (SPSO) and MICROPSO with 30 particles. The advantage becomes more obvious with the population size enlarging.

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Power system reactive power optimization based on improved PSO

Cited by 3 publications

References 5 publications

Reactive Power Optimization Algorithm of Particle Swarm Optimization with Sensitivity Analysis

Reactive Power Optimization Algorithm of Particle Swarm Optimization with Sensitivity Analysis

Improved Embedded Micro-PSO for High-Dimensional Optimization Problems

Improved MICROPSO Algorithm and Its Application on Reactive Power Optimization

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