Comparative Analysis of Fractional-Order PID Controller for Pitch Angle Control of Wind Turbine System

Karad, Shivaji; Thakur, Ritula

doi:10.1007/978-981-32-9515-5_61

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…The right controllers are required to control the fluid's volume. A controller's primary function in a system is to maintain a height at the selected point and make it adaptable to the new set point [7].…”

Section: Types Of Industrial Tank Systemsmentioning

confidence: 99%

Modeling and performance analysis of FOPID controller for interacting coupled tank system

Ramesh,

Bharatiraja,

Teka

et al. 2023

FME Transactions

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Process control is fundamental in modern interaction since it ensures security and improvement in a cycle. Furthermore, process control is a valuable apparatus to fulfill the ecological strategy and item quality necessities. In ventures, one of the controlling system factors is fluid level, the fluid level regulators are a critical concern and well-known interaction, and the aggregate illustrative additionally genuine world in designing techniques. Fluid-level coupled tank framework can be set up into two popular types of interfacing and non-associating structure. This work centers around associating coupled tank control frameworks, numerous issues impacting the fluid level like nonlinearity of the framework, displaying vulnerabilities, and complex investigation, so to conquer those issues, to acquire steady stable results and quick reactions different regulators are required. The liquid must be transferred and kept in a holder for control design in the modern day. The study of a partial request proportional-integralderivative (PID) regulator for controlling a fluid level of the tank framework is presented in this work. FOPID and TID controller techniques are tested and demonstrated for coupling connected tank systems using several partial request regulators, including Commande Robuste d'Ordre Non-Entire (CRONE), Tilt-Integral Derivative (TID), and Fractional order PID (FOPID). The result reaction is directed with the MATLAB®/Simulink® circumstance to check the exhibition of the framework. The reproduction results showed that by controlling connecting coupled tank system (CTS) without aggravation, the reaction is great, however remembering outside unsettling influence for the subsequent tank, the regulator shows a feeble reaction aside from the FOPID regulator. The explanation is FOPID regulator has at least two changed boundaries that expand the vigor of the framework. From the regulators tried in this work, the partial request relative basic subordinate regulator (FOPID) has great execution contrasted with PID, TID, and digital-PID regulators. The accomplished presentation particularly of the FOPID regulator is a better performance for CTS compared to the other listed controllers.

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Section: Types Of Industrial Tank Systemsmentioning

confidence: 99%

Modeling and performance analysis of FOPID controller for interacting coupled tank system

Ramesh,

Bharatiraja,

Teka

et al. 2023

FME Transactions

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“…The PID controller and the parameters involved with this controller are discussed in many research papers in the literature [43][44][45]. Many methods provide PID controller tuning in [46,47]. A typical PID controller arrangement can be defined by three-terms including P, I, and D controllers, where the transfer function is discussed in [48,49] along with parallel and ideal forms as given in Equations ( 22) and (23).…”

Section: Controllers Validation and Implementationmentioning

confidence: 99%

Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

Bharti

Sarita

Vardhan³

et al. 2021

IET Renewable Power Gen

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This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The fractional calculus assigns an arbitrary fractional order to derivatives and integrals terms. These fractional-order systems can be described by a Linear Time Invariant (LTI) fractional-order differential equation [ 23 , 24 , 25 ]. The traditional fractional order transfer function is given in Eq.…”

Section: Blade Pitch Angle Controllermentioning

confidence: 99%

Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller

Shawqran

El-Marhomy

Attia

et al. 2021

Heliyon

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Wind energy is considered as one of the rapidest rising renewable energy systems. Thus, in this paper the wind energy performance is enhanced through using a new adaptive fractional order PI (AFOPI) blade angle controller. The AFOPI controller is based on the fractional calculus that assigns both the integrator order and the fractional gain. The initialization of the controller parameters and the integrator order are optimized using the Harmony search algorithm (HSA) hybrid Equilibrium optimization algorithm (EO). Then, the controller gains (K p ; K i ) are auto-tuned. The validation of the new proposed controller is carried out through comparison with the traditional PID and the Adaptive PI controllers under normal and fault conditions. The fractional adaptive PI improved the wind turbine's electrical and mechanical behaviors. The adaptive fractional order PI controller has been subjected to other high variation wind speed profiles to prove its robustness. The controller showed robustness to the variations in wind speed profile and the nonlinearity of the system. Also, the proposed controller (AFOPI) assured continuous wind power generation under these sharp variations. Moreover, the active power statistical analysis of the AFOPI showed increase in energy captured of around 25 %, and reduction in the standard deviation and root mean square error of around 10% compared to the other controllers.

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Comparative Analysis of Fractional-Order PID Controller for Pitch Angle Control of Wind Turbine System

Cited by 7 publications

References 12 publications

Modeling and performance analysis of FOPID controller for interacting coupled tank system

Modeling and performance analysis of FOPID controller for interacting coupled tank system

Controller design for DFIG‐based WT using gravitational search algorithm for wind power generation

Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller

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