ANFIS Wind Speed Estimator-Based Output Feedback Near-Optimal MPPT Control for PMSG Wind Turbine

Phan, Nhat Minh-Le; Tung, Duong-Nguyen; Mai, Thanh T.; Vu, Nga Thi-Thuy

doi:10.1007/s40313-022-00980-5

Cited by 3 publications

(5 citation statements)

References 35 publications

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“…[17][18][19] The optimal control technique is extensively utilized in the domain of WEGS to address the challenges associated with MPPT control. [20][21][22][23][24][25][26] The work in Reference 21 presents the utilization of an adaptive neuro-fuzzy inference system (ANFIS) estimator and a near-optimal controller for wind speed sensorless MPPT control of the WEGS. The ANFIS-based estimator is employed to approximate the wind speed, thereby obviating the need for an anemometer.…”

Section: Vumentioning

confidence: 99%

“…This mechanical power is then fed to the generator to generate electricity. The mechanical power which is extracted from the wind is expressed as the following 21 :…”

Section: Wind Turbine Modelmentioning

confidence: 99%

“…where R is the rotor radius, 𝜌 is the air density, 𝜔 r is rotor speed, 𝜔 g is generator speed, V is the wind speed, C p is the power conversion coefficient of wind turbine. The value of C p depends on 𝛽 and 𝜆 as follow 21 :…”

Section: Wind Turbine Modelmentioning

confidence: 99%

“…The mechanical power which is extracted from the wind is expressed as the following 21 :

{P}_r=\frac{1}{2}\pi \rho {R}^2{V}^3{C}_p\left(\lambda, \beta \right),

where R is the rotor radius,

\rho

is the air density,

{\omega}_r

is rotor speed,

{\omega}_g

is generator speed, V is the wind speed,

{C}_p

is the power conversion coefficient of wind turbine. The value of

{C}_p

depends on

\beta

and

\lambda

as follow 21 :

{C}_p=0.22\left(116{\lambda}_t-0.4\beta -5\right){e}^{-12.5{\lambda}_t},

in which

λ_{t} = \frac{1}{λ + 0.08 β} prefix- \frac{0.035}{}

…”

Section: System Modelingmentioning

confidence: 99%

“…Optimal control is also a favored method for enhancing control system efficiency 17–19 . The optimal control technique is extensively utilized in the domain of WEGS to address the challenges associated with MPPT control 20–26 . The work in Reference 21 presents the utilization of an adaptive neuro‐fuzzy inference system (ANFIS) estimator and a near‐optimal controller for wind speed sensorless MPPT control of the WEGS.…”

Section: Introductionmentioning

confidence: 99%

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Optimal captured power control of variable speed wind turbine systems: Adaptive dynamic programming approach

2024

Adaptive Control & Signal

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SummaryAn adaptive optimal controller is proposed in this paper to maximize the captured power of a variable speed wind power system. The proposed controller is a combination of optimal and adaptive control components. The Adaptive Dynamic Programming technique is used to design the optimal control component to overcome the nonlinear problem of system dynamics and ensure stability. While the neural network is used to approximate unknown disturbances and system uncertainties. After that, the adaptive control component fully compensates for the effects of these unknown elements. Neither optimal nor adaptive control components necessitate prior knowledge of system dynamics. Furthermore, the approximation network updates only the weight matrix norm rather than the weight matrix of the neural network in each interval time, which significantly reduces computation. The stability analysis of the closed‐loop system is obtained using Lyapunov stability theory. The correctness and robustness of the control scheme are validated in two different scenarios using MATLAB/Simulink. The presented robust adaptive optimal controller is also compared to other existing controllers to demonstrate its benefits.

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Section: Vumentioning

confidence: 99%

“…This mechanical power is then fed to the generator to generate electricity. The mechanical power which is extracted from the wind is expressed as the following 21 :…”

Section: Wind Turbine Modelmentioning

confidence: 99%

Section: Wind Turbine Modelmentioning

confidence: 99%

“…The mechanical power which is extracted from the wind is expressed as the following 21 :

{P}_r=\frac{1}{2}\pi \rho {R}^2{V}^3{C}_p\left(\lambda, \beta \right),

where R is the rotor radius,

\rho

is the air density,

{\omega}_r

is rotor speed,

{\omega}_g

is generator speed, V is the wind speed,

{C}_p

is the power conversion coefficient of wind turbine. The value of

{C}_p

depends on

\beta

and

\lambda

as follow 21 :

{C}_p=0.22\left(116{\lambda}_t-0.4\beta -5\right){e}^{-12.5{\lambda}_t},

in which

λ_{t} = \frac{1}{λ + 0.08 β} prefix- \frac{0.035}{}

…”

Section: System Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal captured power control of variable speed wind turbine systems: Adaptive dynamic programming approach

2024

Adaptive Control & Signal

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An Application on Supplying Türkiye'S Electricity Demand with Hydrogen: Demand Forecasting with Anfis

ŞEKERCİ,

SONER KARA

2024

Preprint

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Técnicas de control inteligente para el seguimiento del punto de máxima potencia en turbinas eólicas

Muñoz-Palomeque,

Sierra-García,

Santos

2024

Rev. iberoam. autom. inform. ind.

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El seguimiento del punto de máxima potencia (MPPT) es una etapa esencial en la operación de las turbinas eólicas para garantizar una generación de energía eficiente. En los últimos años se han diseñado y aplicado técnicas de control avanzadas para lograr este objetivo, solventando algunas de las limitaciones de los métodos clásicos. Este artículo proporciona una visión general de las estrategias existentes y describe con más detalle algunas configuraciones de control específicas, explicando su utilidad y proporcionando una base para futuros desarrollos. En concreto incluye técnicas de control basadas en inteligencia artificial para el estudio del control MPPT en aerogeneradores. Se ejemplifican dos estrategias de control inteligente: una red neuronal y un controlador de lógica borrosa. Estos enfoques se enmarcan en la regulación del par electromagnético del generador y, en consecuencia, de la velocidad angular del sistema, mejorando la generación de potencia. Los resultados evidencian los beneficios de estos controladores inteligentes para maximizar la potencia y mejorar el proceso de conversión de energía.

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ANFIS Wind Speed Estimator-Based Output Feedback Near-Optimal MPPT Control for PMSG Wind Turbine

Cited by 3 publications

References 35 publications

Optimal captured power control of variable speed wind turbine systems: Adaptive dynamic programming approach

Optimal captured power control of variable speed wind turbine systems: Adaptive dynamic programming approach

An Application on Supplying Türkiye'S Electricity Demand with Hydrogen: Demand Forecasting with Anfis

Técnicas de control inteligente para el seguimiento del punto de máxima potencia en turbinas eólicas

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