Open AccessComparative study on the performance of many‐objective and single‐objective optimisation algorithms in tuning load frequency controllers of multi‐area power systems
Author(s) -
Hajiakbari Fini Masoud,
Yousefi Gholam Reza,
Haes Alhelou Hassan
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2015.1334
Subject(s) - robustness (evolution) , minification , control theory (sociology) , electric power system , computer science , cluster analysis , automatic frequency control , power (physics) , algorithm , mathematical optimization , control (management) , mathematics , telecommunications , physics , quantum mechanics , artificial intelligence , biochemistry , chemistry , machine learning , gene
Load frequency control is among the most important control tasks in power systems operation. Many researchers have focused on tuning the load frequency controllers using single‐objective evolutionary algorithms. To avoid the drawbacks of single‐objective optimisation algorithms, in this paper, tuning the load frequency controllers is modelled as a many‐objective (MO) minimization problem. This MO optimisation problem is solved using an MO optimisation algorithm with clustering‐based selection. Considering the maximum value of each objective among the non‐dominated solutions found by the MO optimisation algorithm, the worst solution is determined. To select one of the obtained non‐dominated solutions as the controllers’ parameters, a strategy based on the maximum distance from the worst solution is proposed. In order to measure the effectiveness of the proposed MO technique against several recently proposed single‐objective optimisation algorithms, for tuning load frequency controllers, comparative simulation studies are carried out on two different test systems. Simulation results show that, in terms of different performance indices, the controllers designed by the proposed MO method are far superior to the controllers designed with the single‐objective optimisation algorithms. Also, the presented results confirm the robustness of the controllers designed by the proposed method in case of power system parameters variations.