2021
DOI: 10.1016/j.heliyon.2021.e08239
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Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller

Abstract: 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 (… Show more

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Cited by 7 publications
(3 citation statements)
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“…In order to enhance the control performances of a hybrid power system under a wide range of environmental conditions, the chaotic GWO has been utilized by [32] introduced the adaptive control and fuzzy neural network control techniques for single-phase inverters to improve voltage tracking performance and keep its robustness higher when sources of uncertainty are present in the Photovoltaic (PV) systems. Furthermore, an adaptive PI controller is used in [33] to reinforce the DClink voltage in a single-stage PV system linked to the grid [34] have conducted several comparative studies between conventional PID and fuzzy controllers to demonstrate the superiority of fuzzy controllers. A real-time implementation of an intelligent Fuzzy PI Regulator based on 33 level-switched multilevel capacitor inverters for permanent magnet synchronous motor (PMSM) drives has been suggested by [35].…”
Section: Literature Reviewmentioning
confidence: 99%
“…In order to enhance the control performances of a hybrid power system under a wide range of environmental conditions, the chaotic GWO has been utilized by [32] introduced the adaptive control and fuzzy neural network control techniques for single-phase inverters to improve voltage tracking performance and keep its robustness higher when sources of uncertainty are present in the Photovoltaic (PV) systems. Furthermore, an adaptive PI controller is used in [33] to reinforce the DClink voltage in a single-stage PV system linked to the grid [34] have conducted several comparative studies between conventional PID and fuzzy controllers to demonstrate the superiority of fuzzy controllers. A real-time implementation of an intelligent Fuzzy PI Regulator based on 33 level-switched multilevel capacitor inverters for permanent magnet synchronous motor (PMSM) drives has been suggested by [35].…”
Section: Literature Reviewmentioning
confidence: 99%
“…The model described by ()–() is utilised to represent the operation of the wind turbine [31], PTbadbreak=120.33emAρCp()λ,βpvw3$$\begin{equation}{P}_T = \frac{1}{2}\ A\rho {C}_p\left( {\lambda ,{\beta }_p} \right)v_w^3\end{equation}$$ Cp0.33em()λ,βpbadbreak=c10.33em()c2λigoodbreak−c3βpc4ec5λigoodbreak+c6λ$$\begin{equation}{C}_p\ \left( {\lambda ,{\beta }_p} \right) = {c}_1\ \left( {\frac{{{c}_2}}{{{\lambda }_i}} - {c}_3{\beta }_p - {c}_4} \right){e}^{ - \frac{{{c}_5}}{{{\lambda }_i}}} + {c}_6\lambda \end{equation}$$ 1λi0.33embadbreak=1λ+0.08βp0.33emgoodbreak−0.035βp3+1$$\begin{equation}\frac{1}{{{\lambda }_i}}\ = \frac{1}{{\lambda + 0.08{\beta }_p}}\ - \frac{{0.035}}{{\beta _p^3 + 1}}\end{equation}$$where, PT${P}_T$ is the turbine mechanical power, A is the turbine area, ρ0.33em$\rho \ $is the air density, Cp${C}_p$ is the turbine performance coefficient, vw${v}_w$ is the wind speed, λ is the blade tip speed, βp${\beta }_p$ is the pitch angle and Tm${T}_m$ ...…”
Section: Wecs Elementsmentioning
confidence: 99%
“…The idea of FOPI depends mainly on the classical PI, a field of mathematics called Fractional calculus (FC) that 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 [26]. The traditional fractional order transfer function is given in Eq.…”
Section: Fractional Order Pi Controllermentioning
confidence: 99%