Dependable Parallel Multi-population Different Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control in Electric Power Systems

被引：0

作者：

Yoshida, Hotaka ^{[1
]}

Fukuyama, Yoshikazu ^{[1
]}

机构：

[1] Meiji Univ, Sch Interdisciplinary Math Sci, Tokyo, Japan

来源：

2017 IEEE 10TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (IWCIA) | 2017年

关键词：

Voltage and reactive power control; Parallel and distributed computing; Multi-population; Dependability; Different Evolutionary Particle Swarm Optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents dependable parallel multipopulation differential evolutionary particle swam optimization (DEEPSO) for voltage and reactive power control (VRPC) in electric power systems. Considering large penetration of renewable energies and deregulated environment of power systems, VRPC requires fast computation even for larger-scale problems. One solutions to increase the computation speed is to use parallel and distributed computing. Since power system is one of the infrastructures of social community, not only fast computation, but also sustainable control (dependability) is strongly required for VRPC. A multi-population model is known to be one of the techniques to improve solution quality. The simulation results with IEEE 118 bus systems indicate that dependable parallel multi-population DEEPSO is superior to parallel DEEPSO using a master-slave model especially for dependability on VRPC.

引用

页码：19 / 24

页数：6

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