Optimal PID power system stabilizer tuning based on particle swarm optimization

Boroujeni, Sayed Mojtaba Shirvani

doi:10.17485/ijst/2011/v4i4.4

Cited by 7 publications

(3 citation statements)

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“…Where, Tem represents electromagnetic torque in Nm, Tl represents load torque in Nm, Jm represents rotor moment of inertia in kgm 2 , and B represents viscous friction coefficient in Nm/rads -1 . Substituting Tem = Kti, where Kt represents the torque constant in Nm/A, and I represent the steady phase current in ampere, in (7), we get (8).…”

Section: Modelling Of Bldc Motormentioning

confidence: 99%

“…Metaheuristic or intelligent algorithms are being mostly used in engineering fields with domains including power system [8], [9], electrical drives [5], [7] industrial engineering [10], and mechanical engineering [11], [12]. However, literature review reveals that various optimization algorithms do exist to optimize any controller for solving any real-world application.…”

Section: Introductionmentioning

confidence: 99%

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An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

Sarma,

Bardalai

2023

IJPEDS

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Tuning of proportional-integral-derivative (PID) controller remains a matter of great concern for the control engineers as it plays a major role to obtain optimal performance of any system Due to their simplicity and excellent efficiency, metaheuristic algorithms have recently become extremely popular among researchers for handling a wide range of real-world optimization challenges. In order to optimize a PID controller for managing the speed of a BLDC motor, this work proposes a novel application of the driving training-based optimization (DTBO) algorithm, one of the latest and most recent human-based metaheuristic algorithms. The purpose of this present study is to optimize a PID controller for a BLDC motor speed control by DTBO method and evaluate its performance with a similar controller tuned by grey wolf optimization (GWO) method. Additionally, the suggested DTBO-PID controller's robustness analysis is being carried out with BLDC motor parameter modifications as well as a comparison to the GWO-PID controller. The comparison is carried out in MATLAB/Simulink, and the results are based on common step response metrics such rise time, settling time, and maximum overshoot. For easier comprehension, the results are presented in tabular and graphical form. The chosen BLDC motor drive system's selected DTBO-PID controller performs better and is more reliable than the GWO-PID controller, according to the final simulation findings.

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Section: Modelling Of Bldc Motormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

Sarma,

Bardalai

2023

IJPEDS

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“…Since power systems are highly non-linear systems, with configurations and parameters which alter through time, the CPSS design based on the linearized model of the power system cannot guarantee its performance in a practical operating environment. Therefore, an adaptive PSS which considers the nonlinear nature of the plant and adapts to the changes in the environment is required for the power system [3]. In order to improve the performance of CPSSs, numerous techniques have been proposed for designing them, such as intelligent optimization methods [4] [5] and [6] and fuzzy logic method [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Power System Stabilizer Design using Genetic Algorithms and Particle Swarm Optimization

Eddine,

Abdellatif,

Abdelkader

2023

Control Syst. Optim. Lett.

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in this paper, Meta-heuristic techniques using genetic algorithms GA and particle swarm optimization PSO to tuning optimal design of power system stabilizer PSS proposed. This latter have been used for many years to add damping to electromechanical oscillations of power system, Based on this idea we have proposed multiobjective function composed with tow function, first maximize stability margin by increasing the damping factors while minimizing the real parts of the eigenvalues. Simulation results to comparative study between genetic algorithms and particle swarm optimization obtained by our realized graphical user interface (GUI) proved the efficiency of PSS optimized by genetic algorithms in comparison with Particle Swarm Optimization, showing stable system responses almost insensitive to large parameter variations and under different operating regime (under-excited, nominal and over excited regime).

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