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

被引：2
|
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.
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页数：7
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