A hybrid modified grey wolf optimization‐sine cosine algorithm‐based power system stabilizer parameter tuning in a multimachine power system. (26th March 2020)

• Record Type: Journal Article
• Authors: Devarapalli, Ramesh; Bhattacharyya, Biplab
• Is Part Of: Optimal control applications and methods. Volume 41:Number 4(2020)
• Page Start: 1143
• Page End: 1159
• Publication Date: 2020-03-26
• Subjects: hybrid optimization algorithm -- interarea oscillations -- low‐frequency oscillation damping -- modified grey wolf optimization -- power system stabilizer -- sine‐cosine algorithm; Control theory -- Periodicals; Mathematical optimization -- Periodicals; 629.8312
• DOI: 10.1002/oca.2591
• Languages: English
• ISSNs: 0143-2087

Abstract

Summary: Damping of low‐frequency oscillations due to the unpredictable perturbations of a power network has always been a challenging task. In an interconnected power network, power system stabilizers (PSSs) are in practice to damp out these low‐frequency oscillations by providing a necessary control signal to the automatic voltage regulator unit based on the deviation in generator speed/power output. This article proposes a novel approach of hybrid modified grey wolf optimization‐sine cosine algorithm for tuning the parameters of PSS of an interconnected multimachine power system. The optimal parameter tuning of PSS with the proposed algorithm is achieved by considering a multiobjective function comprises of improving the damping and eigenvalue characteristics of the consolidated multimachine system. A benchmark model of two area four machine system is adopted to investigate the performance achieved with the proposed algorithm in the simultaneous damping of the local and interarea mode of oscillations in a multimachine power system. The system study has been carried out under a self‐clearing fault condition, and the detailed analysis is presented by analyzing the eigenvalues, and their corresponding natural frequencies, damping ratios. The damping nature achieved for the system states under system uncertainties with the proposed algorithm is also presented. The performance obtained from the proposed hybrid algorithm has been compared with the standalone and state‐of‐the‐art optimization methods.