Power optimisation of wind energy conversion system using T-S fuzzy approach

Abderrahim, Sahbi; Allouche, Moez; Chaabane, Mohamed

doi:10.1504/ijise.2021.115073

Cited by 1 publication

(5 citation statements)

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“…The remarkable advantage of this strategy is that the T-S controller and the stability conditions can be determined by utilizing Lyapunov's theory. 28,29 In this context, and according to Equation ( 13), the input matrix BðxÞ contains two nonlinear terms, which are expressed as:…”

Section: Fuzzy Modeling Of the Boost Convertermentioning

confidence: 99%

“…Now, to get the MPP tracking with high accuracy, the state variables xðtÞ should track its reference values x r ðtÞ for all wind speed changes. Besides, to guarantee a soft reference tracking regardless of the rapid wind speed changes, which is viewed as a disturbance, the H performance counts on the tracking error e t ¼ x r ðtÞ À xðtÞ is applied in this study as follows 23,28 :…”

Section: T-s Reference Modelmentioning

confidence: 99%

“…Theorem 1. Consider the wind conversion system (13) controlled by the T-S control law (28), the MPP operation is achieved and the H criterion with the attenuation level γ is verified if there are three matrices X 1 ¼ P À1 1 > 0, P 2 > 0, Y i and a positive value μ satisfying the following LMI optimization problem:…”

Section: Lmi Formulation For H Performancementioning

confidence: 99%

“…It is able to present the dynamic behavior through a combination of linear subsystems linked by a weighting function. The remarkable advantage of this strategy is that the T‐S controller and the stability conditions can be determined by utilizing Lyapunov's theory 28,29 . In this context, and according to Equation (13), the input matrix

B false(x false)

contains two nonlinear terms, which are expressed as:

({, \begin{matrix} center \\ array z_{1} (t) = V_{c h} (t) \\ array z_{2} (t) = i_{L} (t) \end{matrix})

where

z_{k} false(t false) = x_{k} false(t false) \in ([], \begin{matrix} center center \\ array z_{k \min} & array z_{k \max} \end{matrix}); k = 1,2

Then, based on (18), the membership functions are expressed by the following structure:

({, \begin{matrix} center \\ array G_{(1,2), \min} (z (t)) = \frac{z_{(1,2)} (t) - z_{(1,2), \min}}{z_{(1,2), \max} - z_{(1,2), \min}} \\ array G_{(1,2), \max} (z (t)) = 1 - G_{(1,2), \min} (z (t)) \end{matrix})

…”

Section: T‐s Fuzzy Controller Designmentioning

confidence: 99%

“…Finally, the overall T‐S reference model is deducted as:

{\overset{x}{true}}_{r} false(t false) = {falsefalse}_{\sum}^{k = 1} h_{r, k} false(z_{r} false(t false) false) false(A_{r, k} x_{r} false(t false) + r false(t false) false)

Now, to get the MPP tracking with high accuracy, the state variables

x false(t false)

should track its reference values

x_{r} false(t false)

for all wind speed changes. Besides, to guarantee a soft reference tracking regardless of the rapid wind speed changes, which is viewed as a disturbance, the

H_{\infty}

performance counts on the tracking error

e_{t} = x_{r} false(t false) - x false(t false)

is applied in this study as follows 23,28 :

\begin{matrix} right & left {true\int}_{0}^{t f} {(x_{r} (t) - x (t))}^{T} Q (x_{r} (t) - x (t)) d t \leq γ^{2} \\ right & left {true\int}_{0}^{t f} (r (t) r^{T} (t)) + w (t) w^{t} (t)) d t \end{matrix}

where

γ

is a defined constant and

Q

...…”

Section: T‐s Fuzzy Controller Designmentioning

confidence: 99%

See 4 more Smart Citations

Intelligent power control of wind conversion system based on Takagi–Sugeno fuzzy model

Abderrahim

Allouche

Chaabane

2022

Circuit Theory & Apps

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This paper comes up with a novel fuzzy control design for a wind conversion system (WCS). To do this, a boost converter is employed to adjust the rectified voltage under different wind speed levels. Initially, the boost converter model is well presented via the Takagi-Sugeno approach. Then, a T-S fuzzy controller is designed to promptly calculate the duty cycle required for an optimal power operation. Afterward, the optimal DC current is determined and then transferred to the reference model to instantly determine the desired trajectories. These latter should be preserved so as to reach the maximum power. Following that, linear matrix inequalities (LMIs) are also used to guarantee the stability analyses and the H performance. Finally, the sound performance and the validity of the suggested control schema are both proved by a numerical and some experimental results.

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Section: Fuzzy Modeling Of the Boost Convertermentioning

confidence: 99%

Section: T-s Reference Modelmentioning

confidence: 99%

Section: Lmi Formulation For H Performancementioning

confidence: 99%

B false(x false)

contains two nonlinear terms, which are expressed as:

({, \begin{matrix} center \\ array z_{1} (t) = V_{c h} (t) \\ array z_{2} (t) = i_{L} (t) \end{matrix})

where

z_{k} false(t false) = x_{k} false(t false) \in ([], \begin{matrix} center center \\ array z_{k \min} & array z_{k \max} \end{matrix}); k = 1,2

Then, based on (18), the membership functions are expressed by the following structure:

({, \begin{matrix} center \\ array G_{(1,2), \min} (z (t)) = \frac{z_{(1,2)} (t) - z_{(1,2), \min}}{z_{(1,2), \max} - z_{(1,2), \min}} \\ array G_{(1,2), \max} (z (t)) = 1 - G_{(1,2), \min} (z (t)) \end{matrix})

…”

Section: T‐s Fuzzy Controller Designmentioning

confidence: 99%

“…Finally, the overall T‐S reference model is deducted as:

{\overset{x}{true}}_{r} false(t false) = {falsefalse}_{\sum}^{k = 1} h_{r, k} false(z_{r} false(t false) false) false(A_{r, k} x_{r} false(t false) + r false(t false) false)

Now, to get the MPP tracking with high accuracy, the state variables

x false(t false)

should track its reference values

x_{r} false(t false)

for all wind speed changes. Besides, to guarantee a soft reference tracking regardless of the rapid wind speed changes, which is viewed as a disturbance, the