Design of Proportional-Integral-Derivative Controller Using Stochastic Particle Swarm Optimization Technique for Single-Area AGC Including SMES and RFB Units

2015 ◽  
pp. 299-309 ◽  
Author(s):  
K. Jagatheesan ◽  
B. Anand ◽  
Nilanjan Dey ◽  
M. A. Ebrahim
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Particle Swarm Optimization Technique ◽  
Stochastic Particle

scholarly journals Proposed particle swarm optimization technique for the wind turbine control system

2020 ◽  
Vol 53 (5-6) ◽  
pp. 1022-1030 ◽  
Author(s):  
Atif Iqbal ◽  
Deng Ying ◽  
Adeel Saleem ◽  
Muhammad Aftab Hayat ◽  
Muhammad Mateen
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Clean Energy ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Particle Swarm Optimization Technique

Wind energy is a useful and reliable energy source. Wind turbines are attracting attention with the dependency of the world on clean energy. The turbulent nature of wind profiles along with uncertainty in the modeling of wind turbines makes them more challenging for prolific power extraction. The pitch control angle is used for the effective operation of wind turbines at the above-nominal wind speed. To extract stable power as well as to keep wind turbines in a safe operating region, the pitch controller should be intelligent and highly efficient. For this purpose, proportional–integral–derivative controllers are mostly used. The parameters for the proportional–integral–derivative controller are unknown and calculated by numerous techniques, which is a quite cumbersome task. In this research, the particle swarm optimization technique is used but the conventional particle swarm optimization technique cannot tackle the system’s nonlinearity and uncertainties. Hence, the proposed particle swarm optimization algorithm is employed for the calculation of the controller’s optimal parameters. The proposed technique is implemented on a 5-MW wind turbine, which is designed using the Bladed software. Simulation is performed using MATLAB/Simulink to validate the effectiveness of the proposed technique. A variable wind profile is fed as input into the system and the proposed controller provides satisfactory results for the power, rotor speed, and torque. The system is stable and the settling time is reduced.

scholarly journals Vibration suppression of the horizontal flexible plate using proportional–integral–derivative controller tuned by particle swarm optimization

2021 ◽  
pp. 146134842093463
Author(s):  
M Sukri Hadi ◽  
Intan ZM Darus ◽  
Mat H Ab.Talib ◽  
Hanim M Yatim ◽  
M Osman Tokhi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Vibration Suppression ◽  
Particle Swarm ◽  
Flexible Plate ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation.

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