Adaptive virtual-inertia control and chicken swarm optimizer for frequency stability in power-grids penetrated by renewable energy sources

被引:25
作者
Othman, Ahmed M. [1 ,2 ]
El-Fergany, Attia A. [1 ]
机构
[1] Zagazig Univ, Fac Engn, Dept Elect Power & Machines, Al Bahr St, Zagazig 44519, Sharkia Governo, Egypt
[2] UOIT, Fac Energy Syst & Nucl Sci, Oshawa, ON, Canada
关键词
Frequency control; Adaptive virtual-inertia control; Optimization algorithms; Renewable energy sources; DIFFERENTIAL EVOLUTION; FUZZY-LOGIC; SYSTEMS; ALGORITHM;
D O I
10.1007/s00521-020-05054-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this article, a control scheme based on chicken swarm optimizer (CSO) in cooperation with adaptive virtual-inertia control (AVIC) is investigated. The proposed control scheme aims at improving the frequency stability of an interconnected power system which is penetrated by renewable energy sources. The CSO is applied to produce the best values of the gains of the adapted standard proportional-integral-derivative (PID) controllers and required parameters of AVICs. Various scenarios are addressed in this study such as applications of sudden step load disturbances and severe variations in the inertia of the system. In addition, realistic conditions such as uncertainties of tidal power source and random load disturbances are demonstrated. Compulsory assessments with subsequent discussions to evaluate the results of the CSO are made. The proposed CSO-AVIC based control method is verified by comparisons with well-matured interesting algorithms such as differential evolution and particle swarm optimizers. Various quality specifications of the dynamic responses and the demonstrated results indicate clearly the viability of the proposed CSO-AVIC based on control scheme. It can be emphasized that the utilization of AVIC along with PID controllers are significantly improved the system dynamic performances and their dynamic response specifications meet the terms of standard acceptable criteria's.
引用
收藏
页码:2905 / 2918
页数:14
相关论文
共 41 条
[1]  
[Anonymous], 2018, MATHWORKS MATLAB 201
[2]   Implementing Virtual Inertia in DFIG-Based Wind Power Generation [J].
Arani, Mohammadreza Fakhari Moghaddam ;
El-Saadany, Ehab F. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (02) :1373-1384
[3]  
Awais M, 2017, ADV INTELLIGENT NETW
[4]   Enhancing Frequency Response Control by DFIGs in the High Wind Penetrated Power Systems [J].
Chang-Chien, Lee-Ren ;
Lin, Wei-Ting ;
Yin, Yao-Ching .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2011, 26 (02) :710-718
[5]   Multiobjective-based optimal allocation scheme for load frequency control [J].
Chen, Chunyu ;
Zhang, Kaifeng ;
Geng, Jian ;
Yuan, Kun ;
Yang, Zhenglin ;
Li, Lu .
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2017, 27 (07)
[6]   Optimal sliding mode control for frequency regulation in deregulated power systems with DFIG-based wind turbine and TCSC-SMES [J].
Dahiya, Preeti ;
Sharma, Veena ;
Naresh, R. .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (07) :3039-3056
[7]   Inertia response and frequency control techniques for renewable energy sources: A review [J].
Dreidy, Mohammad ;
Mokhlis, H. ;
Mekhilef, Saad .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 69 :144-155
[8]  
El-Fergany Attia, 2013, Przeglad Elektrotechniczny, V89, P30
[9]   Efficient frequency controllers for autonomous two-area hybrid microgrid system using social-spider optimiser [J].
El-Fergany, Attia A. ;
El-Hameed, Mohammed A. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2017, 11 (03) :637-648
[10]   Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms [J].
El-Fergany, Attia A. ;
Hasanien, Hany M. .
ELECTRIC POWER COMPONENTS AND SYSTEMS, 2015, 43 (13) :1548-1559