Performance improvement of photovoltaic power systems using an optimal control strategy based on whale optimization algorithm

被引:106
|
作者
Hasanien, Hany M. [1 ]
机构
[1] Ain Shams Univ, Fac Engn, Elect Power & Machines Dept, Cairo 11517, Egypt
关键词
Photovoltaic power systems; Power system control and dynamics; Response surface methodology; Whale optimization algorithm; LOW-VOLTAGE RIDE; POINT TRACKING TECHNIQUES; PV SYSTEM; DESIGN OPTIMIZATION; CAPABILITY;
D O I
10.1016/j.epsr.2017.12.019
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Photovoltaic (PV) installations are consistently increasing all over the world, leading to a high penetration to the electric grid. Tremendous efforts should be exerted to maintain the operation of the PV systems at optimal conditions. This paper introduces an optimal control strategy with the purpose of enhancing the performance of PV systems. This control strategy is based on the proportional-integral (PI) controller, which is designed by using the whale optimization algorithm (WOA). The response surface methodology (RSM) model is established to create the objective function and its constraints. The proposed WOA-based PI controllers are utilized to control the DC chopper and grid-side inverter in order to achieve a maximum power point tracking operation and improve the dynamic voltage response of the PV system, respectively. The effectiveness of the control strategy is tested under different operating conditions of the PV system such as (1) subject the system to symmetrical and unsymmetrical fault conditions, (2) study the system responses under different irradiation and temperature conditions using real data extracted from a field test, and (3) subject the system to a sudden load disturbance in an autonomous operation. This effectiveness is compared with that achieved using the generalized reduced gradient (GRG) algorithm based PI controller. The validity of the proposed control strategy is extensively verified by the simulation results, which are performed using PSCAD/EMTDC environment. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:168 / 176
页数:9
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