Optimal damping for generalized unified power flow controller equipped single machine infinite bus system for addressing low frequency oscillation

Md Maksudur Rahman; Ashik Ahmed; Md Mehedi Hassan Galib; Md Moniruzzaman

doi:10.1016/j.isatra.2021.01.031

Optimal damping for generalized unified power flow controller equipped single machine infinite bus system for addressing low frequency oscillation

ISA Trans. 2021 Oct:116:97-112. doi: 10.1016/j.isatra.2021.01.031. Epub 2021 Jan 28.

Authors

Md Maksudur Rahman¹, Ashik Ahmed², Md Mehedi Hassan Galib³, Md Moniruzzaman⁴

Affiliations

¹ Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Boardbazar, Gazipur, Bangladesh. Electronic address: maksudcueteee08@gmail.com.
² Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Boardbazar, Gazipur, Bangladesh. Electronic address: ashik123@iut-dhaka.edu.
³ Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Boardbazar, Gazipur, Bangladesh. Electronic address: galib2091@iut-dhaka.edu.
⁴ Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Boardbazar, Gazipur, Bangladesh. Electronic address: mzaman02eee@gmail.com.

PMID: 33627255
DOI: 10.1016/j.isatra.2021.01.031

Abstract

Low frequency oscillation (LFO) is one of the major concerns for reliable operation of the power system. This LFO occurs due to the failure of the rotor to supply sufficient damping torque to compensate the imbalance between mechanical input and electrical output. Hence, in this paper, we adopt a third generation flexible AC transmission system (FACTS) device named generalized unified power flow controller (GUPFC) based damping controller in order to investigate its effect for mitigating LFO for an single machine infinite bus (SMIB) system. To find an effective damping controller-optimizer pair, we integrate proportional-integral (PI) or lead-lag as a controller and grey wolf optimizer (GWO), differential evolution (DE), particle swarm optimization (PSO), whale optimization algorithm (WOA), and chaotic whale optimization algorithm (CWOA) as an optimizer. Later, we investigate the performances for the above mentioned controller-optimizer pairs through time domain simulation, eigenvalue analysis, nyquist stability test, and quantitative analysis. Moreover, we carry out two non-parametric statistical tests named as one sample Kolmogorov-Smirnov (KS) test and paired sample t-test to identify statistical distribution as well as uniqueness of our optimization algorithms. Our analyses reveal that the GWO tuned lead-lag controller surpasses all other controller-optimizer combinations.

Keywords: CWOA; DE; Eigenvalue based objective function; GUPFC; GWO; Kolmogorov–Smirnov (KS) test; Lead–lag controller; Low frequency oscillation; PI controller; PSO; Paired sample t-test; SMIB; WOA.