The fuzzy-PID based-pitch angle controller for small-scale wind turbine

Ngo, Quang-Vi; Chai, Yi; Nguyen, Trong-Thang

doi:10.11591/ijpeds.v11.i1.pp135-142

Cited by 20 publications

(15 citation statements)

References 23 publications

(30 reference statements)

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“…Multiplying ( 12) by diag{Z −1 (σ), Z −1 (σ)}, and (13) by diag{Z −1 (σ), Z −1 (σ), I, I}, we have ( 12) and ( 13) are equivalent ( 21) and ( 22), respectively. Theorem 4.. 8 The fuzzy model ( 6…”

Section: Finite Frequency Analysis Theorem 44mentioning

confidence: 99%

Finite frequency H∞control design for nonlinear systems

Lahlou

El-Amrani

Boumhidi

2021

IJPEDS

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Section: Finite Frequency Analysis Theorem 44mentioning

confidence: 99%

Finite frequency H∞control design for nonlinear systems

Lahlou

El-Amrani

Boumhidi

2021

IJPEDS

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“…In that paper, in addition to rotor dynamics, blade and tower dynamics are taken into account. In [13], the pitch angle is controlled by a PID whose parameters are tuned by a fuzzy logic inference system. The effectiveness of the method is tested by the simulation of a small wind turbine.…”

Section: Related Workmentioning

confidence: 99%

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Sierra-García

Santos

2021

Neural Comput & Applic

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This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.

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“…Moreover, fuzzy logic is well suited to modelling a nonlinear system according to [11]- [14]. In this context, Allouche et al [15] designed a Takagi-Sugeno reference fuzzy model, in order to generate the optimal trajectory corresponding to only the maximum power.…”

Section: Introductionmentioning

confidence: 99%

Augmented robust T-S fuzzy control based PMSG wind turbine improved with H∞ performance

Tidjani

Guessoum

2021

IJPEDS

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In this paper, an improved augmented Takagi-Sugeno fuzzy control design applied to the system of converting wind turbine energy was proposed. The wind generator used is based on a permanent magnet synchronous wind power generator (PMSG) under varying operation of the wind speed. The proposed T-S fuzzy control strategy aims to maximize wind energy in low wind speed. A part of our contribution lies in the limitation of the power output of the wind generator in high wind speed. Through the concept of the virtual desired variables, the design of the output tracking controller is achieved. In light of this concept, the developed T-S fuzzy control was designed via parallel-distributed compensation (PDC) approach with H∞ performance.Sufficient conditions for the stability of the closed-loop system affected by external disturbances are proved from Lyapunov’s direct method and the feedback gains of the controller strategy are determined by linear matrix inequalities (LMIs) tools. Another contribution is in showing the robustness of the Takagi-Sugeno based control strategy, with a focus on a set of system parameters with model uncertainties. The simulation results show the high performance of the proposed controller strategy for a 5MW (PMSG) obtained through simulation.

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The fuzzy-PID based-pitch angle controller for small-scale wind turbine

Cited by 20 publications

References 23 publications

Finite frequency H∞control design for nonlinear systems

Finite frequency H∞control design for nonlinear systems

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Augmented robust T-S fuzzy control based PMSG wind turbine improved with H∞ performance

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