Higher performance of the super-twisting sliding mode controller for indirect power control of wind generator based on a doubly fed induction generator

Belabbas, Belkacem; Allaoui, Tayeb; Tadjine, Mohamed; Denaï, Mouloud; Hatfield, U K

doi:10.1109/icee-b.2017.8192006

Cited by 5 publications

(8 citation statements)

References 19 publications

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“…The proposed FTABSMC depicts high chattering (28%–30% standard deviation) with reaching phase transient overshoot (31%) while considering sign function in control derivations (robust control parameter: Equation ) as shown in Figure 10C. But while using tanh function based sliding surface (Equation 29), the control parameters get estimated with less deviation by smooth approximation feature of this function 22 . The chattering is less in proposed FTABSMC (12%–18% standard deviation) with almost negligible overshoot (due to nominal control) as in Figure 10D.…”

Section: Results Analysismentioning

confidence: 99%

“…But while using tanh function based sliding surface (Equation 29), the control parameters get estimated with less deviation by smooth approximation feature of this function. 22 The chattering is less in proposed FTABSMC (12%-18% standard deviation) with almost negligible overshoot (due to nominal control) as in Figure 10D.…”

Section: Case 5: Performance Under Turbulent Wind Speedmentioning

confidence: 87%

“…The tanh is evidenced to be effective for chattering reduction for conventional as well as higher order SMCs. 22 The tanh function in sliding surface depicts less chattering sliding phase (Figure 4) due to smooth approximation property of it, for a per unit (0 to 1 scale) converted system dynamics. This is further discussed in Section 3.2 and elaborated in Section 5.4.2.…”

Section: Ftabsmc For Rsc: Internal P-q Control For Dfigmentioning

confidence: 97%

“…Further, a hyperbolic tangent ( tanh ) function is used for sliding surface design (Equation 29) instead of conventional signature ( sign ) function, for avoiding + ve /– ve error signature fluctuation (contributes the chattering profile by robust control decision). The tanh is evidenced to be effective for chattering reduction for conventional as well as higher order SMCs 22 . For proposed FTABSMC, the tanh function based sliding surface will lead to a less chatter continuous sliding phase by robust control parameters (Equation ).…”

Section: Finite Time Adaptive Backstepping Sliding Mode Control (Ftabmentioning

confidence: 99%

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A finite time adaptive back‐stepping sliding mode control for instantaneous active‐reactive power dynamics based DFIG‐wind generation towards improved grid stability

2020

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In this paper, a low computational, robust nonlinear feedback control is proposed as independent distributed generation controller (IDGC) for doubly fed induction generator (DFIG) based wind power generation system (WPGS). WPGS integration as distributed generation (DG) to grid point of common coupling (PCC) is reflecting high power loss profile in terms of low frequency oscillations under grid operational contingencies and have high instability hazard under unbounded uncertainties like unintentional islanding operation. Thus, to improve the stability margin of DFIG-WPGS integration fast DG dynamic relations are obtained, where instantaneous activereactive power (P-Q) formulation is proposed to avoid unnecessary phase locked loop (PLL) angle (ω) estimation. The feedback path is designed with proposed finite time adaptive backstepping sliding mode control (FTABSMC) for IDGC operation according to ISA-95 standards. The FTABSMC is ensured with bounded/unbounded uncertainty handling capability by incorporating backstepping based dynamic error profile depletion and with enhanced stability margin by finite time sliding surface based error trajectory to equilibrium. Further, the adaptive sliding surface estimation is proposed in terms of dynamic uncertainty (energy fluctuation) for robust unbounded uncertainty handling. The stability improvement is established with small-signal (SS) based closed loop analysis of considered DFIG-WPGS. The uncertainty handling capability is presented thought rigorous case studies in MATLAB based simulation environment and TMS 320 digital signal processor (DSP) based processor-in-loop (PIL) validation. K E Y W O R D S backstepping control, distributed generation, phase locked loop, sliding mode control, wind power generation system 1 | INTRODUCTION To cope with increasing power demand and carbon emission (pollution towards global warming), the renewable energy resource (RES) based distributed generations (DGs) have gained importance in energy market from last few decades. 1 The wind energy (WE) is well utilized as DG for local

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Section: Results Analysismentioning

confidence: 99%

Section: Case 5: Performance Under Turbulent Wind Speedmentioning

confidence: 87%

Section: Ftabsmc For Rsc: Internal P-q Control For Dfigmentioning

confidence: 97%

Section: Finite Time Adaptive Backstepping Sliding Mode Control (Ftabmentioning

confidence: 99%

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A finite time adaptive back‐stepping sliding mode control for instantaneous active‐reactive power dynamics based DFIG‐wind generation towards improved grid stability

2020

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“…Recently, many attempts have been seen trying to overcome the chattering problem while using the SMC methodology in nonlinear control systems [19][20][21][22]. Many researchers have used the conventional first order sliding mode control techniques with updated control functions [23,24].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems

Elmogy¹,

Elawady²

2023

Intelligent Automation &Amp; Soft Computing

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One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with uncertainty. Many researchers have been working on developing such type of controllers. One of the most efficient techniques employed to develop such controllers is sliding mode control (SMC). However, the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical applications. In this paper, the drawbacks of low order traditional sliding mode control (FOTSMC) are resolved by presenting a novel adaptive radial basis function neural network-based generalized r th order sliding mode control strategy for n th order uncertain nonlinear systems. The proposed solution adopts neural networks for their excellent capability in function approximation and thus used to approximate the nonlinearities and uncertainties for systems under consideration. The approximation errors are completely considered in the developed approach. The proposed approach can be used with any order of sliding mode and thus can be generally used with various types of applications. The global stability of the proposed control approach is proved through Lyapunov stability criterion. The proposed approach is validated and assessed through simulations on the nonlinear inverted pendulum system with severe modeling uncertainties. The simulations results show that the proposed approach provide superior performance compared with other approaches in the literature.

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Hierarchical energy management and control to improve the reliability and efficiency of wind farms connected to the grid

Belabbas

Denaï

Allaoui

2020

Int Trans Electr Energ Syst

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Higher performance of the super-twisting sliding mode controller for indirect power control of wind generator based on a doubly fed induction generator

Cited by 5 publications

References 19 publications

A finite time adaptive back‐stepping sliding mode control for instantaneous active‐reactive power dynamics based DFIG‐wind generation towards improved grid stability

A finite time adaptive back‐stepping sliding mode control for instantaneous active‐reactive power dynamics based DFIG‐wind generation towards improved grid stability

Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems

Hierarchical energy management and control to improve the reliability and efficiency of wind farms connected to the grid

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