Open AccessBio‐inspired hybrid BFOA‐PSO algorithm‐based reactive power controller in a standalone wind‐diesel power system
Author(s) -
Wagle Raju,
Sharma Pawan,
Sharma Charu,
Gjengedal Terje,
Pradhan Chittaranjan
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12778
Subject(s) - control theory (sociology) , ac power , particle swarm optimization , pid controller , wind power , electric power system , robustness (evolution) , matlab , controller (irrigation) , computer science , hybrid power , engineering , control engineering , voltage , automotive engineering , power (physics) , algorithm , control (management) , temperature control , agronomy , biochemistry , physics , chemistry , quantum mechanics , artificial intelligence , biology , electrical engineering , gene , operating system
Summary With an increase in the penetration of renewable energy sources such as wind into the power systems, the operation and control of voltage/reactive power have become more complicated and challenging than ever. As a result, the reactive power imbalance between reactive power generation and demand instigates a reduction in system voltage stability. To deal with the aforesaid scenarios, automatic voltage regulator (AVR) and static synchronous compensator (STATCOM) are incorporated to curtail the voltage deviations in a standalone wind‐diesel power system. In this article, a hybrid bacterial foraging optimization algorithm‐particle swarm optimization (hBFOA‐PSO) algorithm is proposed for optimizing the PI controller parameters of AVR and STATCOM to further improve the system voltage/reactive power performance. Additionally, H ∞ ‐loop shaping technique is designed to analyze the performance indexes (ie, robustness and stability) of the presented controller with the aim of handling the unstructured uncertainties from generation and loading situation. In order to present the efficiency of the proposed controllers, the performance of the hBFOA‐PSO controller is compared with the performance of the BFOA, PSO, and modified grey wolf optimization (MGWO)‐based PI controllers for the same wind‐diesel system. The dynamic responses of the wind‐diesel system for different disturbance cases have been investigated in the MATLAB/SIMULINK environment.