A Bacterial Foraging Optimization Technique and Predictive Control Approach for Power Management in a Standalone Microgrid

被引：3

作者：

Dubuisson, F. ^{[1
]}

Chandra, A. ^{[1
]}

Rezkallah, M. ^{[1
,2
]}

Ibrahim, H. ^{[2
]}

机构：

[1] Ecole Technol Super, Dept Elect Engn, Montreal, PQ, Canada

[2] Cegep Sept Iles, Inergia, Sept Iles, PQ, Canada

来源：

2020 IEEE ELECTRIC POWER AND ENERGY CONFERENCE (EPEC) | 2020年

关键词：

Hybrid Standalone Microgrid; Bacterial Foraging Optimization (BFO); Particle Swarm Optimization (PSO); Power Management; Predictive Control; Diesel Generator; Battery Energy Storage System; Renewable Energy Sources;

D O I：

10.1109/EPEC48502.2020.9320038

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, the Bacterial Foraging Optimization (BFO) algorithm is proposed for real-time calculation of active and reactive power references estimation. This method emulates the behavior of E.coli bacteria to find the global minimum of a cost function. Using this technique, one estimates the power references of the system to use as input parameters for the developed predictive control strategy for the three- phase inverter. The comportment of the BFO and the predictive control, are tested using Matlab/Simulink under load variation. In addition, a detailed comparison between the BFO method and Particle Swarm Optimization (PSO) is provided.

引用

页数：7

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