Maximum power point tracking for wind power systems with an improved control and extremum seeking strategy

Summary A novel approach for increasing the sustainability and reliability of energy conversion in a wind turbine (WT) system in the presence of uncertainties/disturbances and sensors fault is proposed. To this aim, an active robust fault tolerant model predictive controller (FT‐MPC) is designed for WTs under partial load condition by combining the advantages of the sliding mode observer (SMO) and MPC approaches. Since the WTs are subject to physical/constructive constraints, first, an online MPC is designed to guarantee all constraints satisfaction. Then, the consequences of sensors fault on the control system's performance are demonstrated. An efficient approach is proposed based on the SMO for estimating the sensors faults and the states simultaneously. The most significant contribution of the observer‐based FT‐MPC controller is that it is not only robust against uncertainties/disturbances but also using the proposed strategy the challenges of nonlinearity, constraints, and sensor faults can be effectively handled.

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“…Nonlinearity of the WT model refers to the aerodynamic torque defined in (6). Linearization of (6) results…”

Section: Linearization Of Modelmentioning

confidence: 99%

“…In this case, the main goal is optimizing the extracted aerodynamic wind power while the rotor tracks the optimal rotor speed. 6,7 Different methods have been already used for WTs control in either full-load or partial-load regions. At first, classical linear/nonlinear control methods were designed for WTs described in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Wind turbines sustainable power generation subject to sensor faults: Observer‐based MPC approach

Ghanbarpour

Bayat

Jalilvand

2019

Int Trans Electr Energ Syst

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“…Aerodynamic power in a wind turbine is equal to Equation :

P_{ωt} = \frac{1}{2} ρ_{ωt} \cdot K_{ωt} \cdot V_{ωt}^{3} \cdot D_{ωt} ((),, λ_{normalωt}, β_{normalωt}) .

…”

Section: Configuration Of the System Modelmentioning

confidence: 99%

“…In this article, relations needed to control the rotor‐side converter are defined as follows:

({,, \begin{array}{l} \frac{normald i_{dsw}}{normald t} = ω_{base} \cdot {(L_{mm}^{2} - L_{ss} \cdot L_{rr})}^{- 1} \cdot ([],, R_{s} L_{rr} i_{dsw} + ((),, ((),, ω_{s} - ω_{r}) \cdot L_{mm}^{2} - ω_{s} L_{rr} L_{ss}) i_{qsw} - R_{r} L_{mm} i_{drw} - ω_{r} L_{rr} L_{mm} i_{qrw} + L_{mm} V_{drw} - L_{rr} V_{dsw}), \\ \frac{normald i_{qsw}}{normald t} = ω_{base} \cdot {(L_{mm}^{2} - L_{ss} \cdot L_{rr})}^{- 1} \cdot ([],, ((),, - ((),, ω_{s} - ω_{r}) \cdot L_{mm}^{2} + ω_{s} L_{rr} L_{ss}) i_{dsw} + R_{s} L_{rr} i_{qsw} + ω_{r} L_{rr} L_{mm} i_{drw} - R_{r} L_{mm} i_{qrw} + L_{mm} V_{qrw} - L_{rr} V_{qsw}), \\ \frac{normald i_{drw}}{normald t} = ω_{base} \cdot {(L_{mm}^{2} - L_{ss} \cdot L_{rr})}^{- 1} \cdot ([],, - R_{s} L_{mm} i_{dsw} + ω_{r}) \end{array})

…”

Section: Configuration Of the System Modelmentioning

confidence: 99%

Predictive control strategy to improve stability of DFIG-based wind generation connected to a large-scale power system

Bagheri

Jalilvand

2016

Int. Trans. Electr. Energ. Syst.

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Summary The present article aims to improve the small signal stability in a large‐scale network using model predictive control (MPC). The predictive strategy is based on the Laguerre function so as to reduce computational time in MPC and increase precise detection of control signals. This strategy is implemented in such a way to control the active and reactive power on rotor‐side converter (RSC) and increase RSC yield in the presence of uncertainties through application of appropriate switching to inverter. Therefore, static synchronous compensator is used in a way that the reactive power is compensated and by designing a damping controller the interarea oscillations will be reduced in New England large‐scale system. The simulation results were evaluated using MATLAB software in the field of time and frequency under different scenarios. Moreover, the proposed method was compared with conventional MPC and proportional‐integral controller while its superiority to stabilize and reduce the computational time is clearly shown.

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“…Therefore, in this region, the objective is to adjust the blades pitch angle to keep the generator's output power and the rotor speed at its rated values. This paper focuses on the controller design for the full load operation of wind turbine.In recent years, numerous control methods have been used and developed to apply wind turbines, eg, PID schemes, [1][2][3][4] linear/nonlinear control, [5][6][7] optimal control, [8][9][10][11] model predictive control, 8,[12][13][14] fuzzy control, 2,3,8,15 and adaptive control. 16,17 Among the existing approaches, the well-known PI/PID controllers are possibly the most widely used approaches because of their simplicity.…”

Section: Introductionmentioning

confidence: 99%

Power regulation and control of wind turbines: LMI‐based output feedback approach

Bayat

Bahmani

2017

Int Trans Electr Energ Syst

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Summary This paper addresses the problem of power regulation and control of wind turbines using an output feedback scheme formulated as a well‐defined linear matrix inequality (LMI) problem. To this aim, first, a piecewise linear (PWL) model is constructed for the wind turbine system with the wind speed as a parameter. Then, using the achieved PWL model a set of output feedback controllers are formulated and solved as an LMI problem. To implement the designed controller in practice, 2 different approaches are proposed, ie, online and off‐line approaches. In the first approach (online), an exact optimal control law is obtained by online evaluation of the PWL model and then solving the associated LMI problem which leads to a relatively high computational complexity. To deal with this computational complexity, an alternative approximate approach is proposed which puts parts of online computations to off‐line precalculation with the cost of sub‐optimality. In this approach, the controller is predesigned for some predefined operating points and then the sub‐optimal controller is achieved by introducing a convex combination of precalculated controllers while guaranteeing the constraints satisfaction. Finally, the performance and characteristics of the proposed approaches are verified using simulation results.

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Maximum power point tracking for wind power systems with an improved control and extremum seeking strategy

Cited by 18 publications

References 14 publications

Wind turbines sustainable power generation subject to sensor faults: Observer‐based MPC approach

Wind turbines sustainable power generation subject to sensor faults: Observer‐based MPC approach

Predictive control strategy to improve stability of DFIG-based wind generation connected to a large-scale power system

Power regulation and control of wind turbines: LMI‐based output feedback approach

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