Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

Abdulhussein, Kareem G.; Yasin, Naseer M.; Hasan, Ihsan Jabbar

doi:10.11591/ijpeds.v12.i2.pp736-744

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…In addition, the BOA has a faster speed of convergence to the optimal solution than the PSO. The performance of the BOA was shown to supersede the performance of the PSO in [39,40]. In particular, the study in [41] justified the applicability of the BOA to an optimal dispatch problem such as ours, and it was proven that the BOA is more efficient than both the PSO and the GA. Consequently, we believe that the BOA can provide the desired efficient optimal solution.…”

Section: Generalized Bo Algorithmmentioning

confidence: 96%

A Flexible Operation and Sizing of Battery Energy Storage System Based on Butterfly Optimization Algorithm

2021

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There is a surge in the total energy demand of the world due to the increase in the world’s population and the ever-increasing human dependence on technology. Conventional non-renewable energy sources still contribute a larger amount to the total energy production. Due to their greenhouse gas emissions and environmental pollution, the substitution of these sources with renewable energy sources (RES) is desired. However, RES, such as wind energy, are uncertain, intermittent, and unpredictable. Hence, there is a need to optimize their usage when they are available. This can be carried out through a flexible operation of a microgrid system with the power grid to gradually reduce the contribution of the conventional sources in the power system using energy storage systems (ESS). To integrate the RES in a cost-effective approach, the ESS must be optimally sized and operated within its safe limitations. This study, therefore, presents a flexible method for the optimal sizing and operation of battery ESS (BESS) in a wind-penetrated microgrid system using the butterfly optimization (BO) algorithm. The BO algorithm was utilized for its simple and fast implementation and for its ability to obtain global optimization parameters. In the formulation of the optimization problem, the study considers the depth of discharge and life-cycle of the BESS. Simulation results for three different scenarios were studied, analyzed, and compared. The resulting optimized BESS connected scenario yielded the most cost-effective strategy among all scenarios considered.

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Section: Generalized Bo Algorithmmentioning

confidence: 96%

A Flexible Operation and Sizing of Battery Energy Storage System Based on Butterfly Optimization Algorithm

2021

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“…The controller mainly controls the position, speed, current and tracking trajectory of permanent magnet DC motor. The simulation outcomes of MATLAB showcase that the optimization performance of butterfly optimization algorithm is better than that of PSO [12]. Zhang R designed a design method of anti-saturation PID current controller for enhancing the dynamic current response speed of PMSM.…”

Section: Related Workmentioning

confidence: 99%

Permanent Magnet Motor Control System Based on Fuzzy PID Control

Sha,

Chen

2024

IJACSA

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Although the traditional permanent magnet synchronous motor control system is simple and convenient, the control of speed and accuracy is often affected by external interference, which impacts the dynamic and static performance requirements. Therefore, this study attempts to introduce fuzzy rules to improve the proportional integral differential control method, and further integrate intelligent optimization algorithms into the fuzzy proportional integral differential control method to construct an efficient and feasible permanent magnet synchronous motor control method. The simulation experiment demonstrates that under fuzzy proportional integral differential control, there is no overshoot in the waveform when facing changes in load, and the tuning time increases from 0.01 seconds to 0.12 seconds. The steady-state error of speed control is small, and there is no obvious oscillation in the waveform. Fuzzy control enhances the control system. After the optimization of the artificial bee colony algorithm, the control system has a faster speed response, with the overshoot diminished from 11.2% to 3.1%, and the adjustment time reduced from 0.27 seconds to 0.19 seconds, enhancing its adaptability. Under load regulation, the optimized control system speed response curve responds in a timely manner without obvious overshooting and oscillating changes. Optimizing variable universe fuzzy proportional integral differential control enables the control system for having better static and dynamic performance, and enhances the adaptability and follow-up of the control system. The current curve starts to stabilize at 0.04s, overcoming the control system oscillations early. The speed response curve and the motor torque curve are improved by the optimized variable domain theory, and the amount of overshoot is significantly reduced. The research and design of a permanent magnet motor control system has practical significance for improving the application performance and adaptability of permanent magnet motors.

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“…An objective function is then calculated for each part, comparing whether the calculated objective value is lower than the particle's personal best. If it is not lower and no action is taken, the personal best position value is assigned as the current position of the particle with the lowest objective value [20][21][22].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Evaluation of Controller Parameters on the Twin Rotor Multiple Input Multiple Output System Using Butterfly-Based Particle Swarm Optimization

ÇABUKER

Almalı

PARLAR

2023

Journal of Scientific Reports-A

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Studies on the control of nonlinear systems with metaheuristic algorithms are increasing day by day. It is one of the nonlinear systems in the Twin rotor multiple input multiple output (TRMS) system, which emerged as a prototype of helicopters. This system has two control angles horizontally and vertically. In this study, the yaw and pitch angle control parameters of the TRMS system were found using both traditional and butterfly-based particle swarm optimization (BFPSO) method. In experimental studies, reference values of main propeller and tail propeller angles were tried to be reached in TRMS with fractional order proportional-integral-derivative (FOPID), proportional-integral-derivative (PID) and tilt-integral-derivative (TID) controllers.

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Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

Cited by 11 publications

References 26 publications

A Flexible Operation and Sizing of Battery Energy Storage System Based on Butterfly Optimization Algorithm

A Flexible Operation and Sizing of Battery Energy Storage System Based on Butterfly Optimization Algorithm

Permanent Magnet Motor Control System Based on Fuzzy PID Control

Evaluation of Controller Parameters on the Twin Rotor Multiple Input Multiple Output System Using Butterfly-Based Particle Swarm Optimization

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