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 ...
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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. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
This paper presents the design of Power System Stabilizers (PSSs) using two Evolutionary Algorithm (EA) techniques, namely;Genetic Algorithm (GA) and Breeder Genetic Algorithm. BGA is a new form of evolutionary algori...
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ISBN:
(纸本)9781424481262
This paper presents the design of Power System Stabilizers (PSSs) using two Evolutionary Algorithm (EA) techniques, namely;Genetic Algorithm (GA) and Breeder Genetic Algorithm. BGA is a new form of evolutionary algorithm, which is based on the idea of survival of the fittest, but differs from the traditional Genetic Algorithm due to its artificial breeding nature. An eigenvalue based objective function is used in the design of the PSSs whereby the algorithms maximize the lowest damping ratio over specified operating conditions. For comparison purpose, the Conventional PSS (CPSS) is also included. The performance and effectiveness of the PSSs in damping the electromechanical modes is investigated. eigenvalue analysis and time domain simulations show that BGA-PSS and GA-PSS perform better than the CPSS for all the operating conditions considered except at the nominal operating condition. However, BGA-PSS performs slightly better than the GA-PSS.
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