Uncertain Saturated Discrete‐Time Sliding Mode Control for A Wind Turbine Using A Two‐Mass Model

A comparative control study for a maximum power tracking strategy of variable speed wind turbine is provided. The system consists of a direct drive permanent magnet synchronous generator (PMSG) and an uncontrolled rectifier followed by a DC/DC switch-mode step down converter connected to a DC load. The buck converter is used to catch the maximum power from the wind by generating an efficient duty cycle. Distinct Maximum Power Point Tracking (MPPT) algorithms are analyzed and compared: a classical Proportional-Integral controller (PI) and two based Fuzzy Logic Controllers (FLC), including a conventional Fuzzy-PI and an Adaptive FLC-PI. The main aim of the presented study is to develop an advanced control scheme for wind generators to ensure a high level operating of the system and a maximum power extraction from the wind. This is achieved by analyzing the behavior of the system under fluctuating wind conditions employing Matlab Simpower Systems tool.Simulation results confirm that the Adaptive FLC-PI controller algorithm has better performances in terms of dynamic response and efficiency especially in comparison with the ones of a PI controller under variable wind speed. KEYWORDS DC-DC buck converter, fuzzy logic controller (FLC), maximum power point tracking (MPPT), permanent magnet synchronous generator (PMSG), proportional integral (PI) controller, wind turbine

Section: Wind System Modelingmentioning

confidence: 99%

Intelligent maximum power tracking control of a PMSG wind energy conversion system

Aicha

Youcef

Hassaine

et al. 2019

“…Since the first derivatives of T a and ω r can be assumed bounded [], it can be readily obtained from

{\overset{ω}{true}}_{r} = false(1 false/ J_{t} false({\overset{T}{true}}_{a} - K_{t} {\overset{ω}{true}}_{r} - {\overset{T}{true}}_{g} false)

that the second derivative of rotor speed ω r is bounded such that

(||, {\overset{ω}{true}}_{r}) \leq B_{r}

, where B r is unknown positive constants. Thus, by applying Young's inequality to , there is

\begin{matrix} right {\dot{V}}_{1} (t) & left \leq - r g (- χ_{m} - k r + k \tilde{r}) - λ {\tilde{x}}^{T} \tilde{x} & right \\ right & left + 2 {\tilde{x}}^{T} P B {\ddot{ω}}_{r} + κ ϱ D_{m} \\ right & left \leq r g χ_{m} + g k r^{2} - r g k c {\tilde{x}}_{2} - λ {\tilde{x}}^{T} \tilde{x} \\ right & left + 2 {\tilde{x}}^{T} P B {\ddot{ω}}_{r} + κ ϱ D_{m} \\ right & left \leq \frac{1}{2} r^{2} + \frac{1}{2} g_{0}^{2} χ_{m}^{2} + g_{m} k r^{2} \\ right & left + \frac{1}{2} g_{0}^{2} c^{2} r^{2} + \frac{1}{2} k^{2} {\tilde{x}}_{2}^{2} - λ {\tilde{x}}^{T} \tilde{x} \\ right & left + 2 {\tilde{x}}^{T} P... \end{matrix}

…”

Section: Appendix Bmentioning

confidence: 99%

“…Since the first derivatives of T a and r can be assumed bounded [2,11,12], it can be readily obtained from̈r = (1∕J t (Ṫ a −K ṫr −Ṫ g ) that the second derivative of rotor speed r is bounded such that | |̈r | | ≤ B r , where B r is unknown positive constants. Thus, by applying Young's inequality to (48), there iṡ (0)) is the initial value.…”

Section: Appendix Bmentioning

confidence: 99%

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Jiao

Yang

Fu³

et al. 2018

As wind energy becomes one of the fastest growing renewable energy resources, the control of large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. In this paper, an adaptive neural pitch angle control strategy is proposed for the variable-speed wind turbines (VSWT) operating in pitch control region. The control objective is to maintain the rotor speed and generator power at the prescribed reference values in the presence of external disturbance, without the need of the information of system parameters and aerodynamics. First, the order of the system dynamics is increased by defining a filtered regulation error. By this means, the non-affine characteristics of the VSWT model is transformed into a simple affine control problem and thus the feedback linearization technique can be employed. The continuousness of control signal is also guaranteed to relax the requirement on the bandwidth of actuators, and the mechanical load on pitching systems is reduced. Subsequently, an online learning approximator (OLA) is utilized to estimate the unknown nonlinear aerodynamics of the wind turbine and extend the practicability of the proposed adaptive parameter-free controller. In addition, a high-gain observer is implemented to obtain an estimation of rotor acceleration, which rejects the need of additional sensors. Rigid theoretical analysis guarantees the tracking of rotor speed/generator power and the boundedness of all other signals of the closed-loop system. Finally, the effectiveness of the proposed scheme is testified via the Wind Turbine Blockset simulation package in Matlab/Simulink environment. Moreover, comparison results reveal that the introduced solution is able to provide better regulation performance than the conventional PI counterpart.

“…The genetic algorithm (GA)-SMC [13], perturbation observed-SMC method [14], inertia emulation-SMC [15] and adaptive backstepping [16] techniques were employed to design a non-linear controller for VSC-HVDC system and responses were observed at different conditions such as short-circuit ratio changes, faults, and power oscillations. The discrete-time SMC is applied to obtain a maximum extraction of wind energy for wind turbine [18]. The discrete-time SMC is applied to obtain a maximum extraction of wind energy for wind turbine [18].…”

Section: Introductionmentioning

confidence: 99%

Stability analysis and a hybrid controller design of grid‐connected offshore wind farm through a VSC‐HVDC transmission link

Srikakulapu

Vinatha

2019

This paper proposes a hybrid controller which is a combination sliding mode control and PI control techniques for AC grid integrated offshore wind farm (OSWF) with voltage source converter -high voltage direct current system. The controller must be capable of controlling AC voltage, DC-link voltage, reactive power and effective power transfer. To examine the FRT capability, a symmetrical fault is applied at onshore AC grid side and compared the performances of the studied system based on the hybrid and PI controllers. The dynamic modelling and linearized system by state-space modelling for the studied system are explained in detail. The small signal stability analysis and controller stability are observed with the help of the eigenvalue analysis. The analysis of the studied system with a hybrid and conventional controllers are conducted in the software environment of the MATLAB/SIMULINK. The effect of parameter uncertainty on total system stability is examined with the help of eigenmatrix of the studied system.