A multi-objective AVR-LFC optimization scheme for multi-area power systems

被引:17
|
作者
Nahas, Nabil [1 ]
Abouheaf, Mohammed [2 ,3 ]
Darghouth, Mohamed Noomane [4 ]
Sharaf, Adel [5 ]
机构
[1] Univ Moncton, Fac Adm, Moncton, NB E1A 3E9, Canada
[2] Bowling Green State Univ, Coll Technol Architecture & Appl Engn, Bowling Green, OH 43402 USA
[3] Aswan Univ, Coll Energy Engn, Elect Engn Dept, Aswan 81528, Aswan Province, Egypt
[4] King Fahd Univ Petr & Minerals, Syst Engn Dept, Dhahran 31261, Eastern, Saudi Arabia
[5] SHARAF Energy Syst Inc, Fredericton, NB E3B 5A3, Canada
关键词
Load frequency control; Automatic voltage regulation; Multi-area power systems; Synchronization schemes; Multi-objective optimization; Non-linear threshold accepting heuristic; LOAD FREQUENCY CONTROL; VOLTAGE REGULATOR AVR; PID CONTROLLER; ALGORITHM; DESIGN;
D O I
10.1016/j.epsr.2021.107467
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
The AVR and LFC schemes are essential power system tools, which are used to support the operation, stability, and security of the generation systems in the main power networks. These control units are designed using numerical as well as analytical approaches. The numerical solutions that are based on heuristics for instance, vary in quality and procedure of the underlying optimization processes leading to the desired outcomes. These processes are mostly used to optimize a single objective criterion that compromises between different performance characteristics like, steady state and transient responses. This work introduces a nonlinear threshold accepting heuristic with an innovative multi-objective optimization architecture. This method is employed to select the control gains of a coupled AVR-LFC scheme for a two-area power system to ensure stable and fast AC generator bus voltage-frequency regulation in react to dynamic disturbances. This approach decides a set of control laws that guarantee fast dynamic response while optimizing conflicting objective criteria. It is validated against other standard heuristics, namely artificial bee colony, differential evolution, and particle swarm optimization in addition to other analytical approaches like internal model-control schemes.
引用
收藏
页数:13
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