Feedforward Feedback Pitch Control for Wind Turbine Based on Feedback Linearization with Sliding Mode and Fuzzy PID Algorithm

Ren, Hongwei; Zhang, Hao; Deng, Guang; Hou, Bin

doi:10.1155/2018/4606780

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…According to the equivalent circuit of the doubly-fed generator and the equations of the stator and rotor, the electromagnetic torque expression is as follows [12].…”

Section: Wind Turbine System Modellingmentioning

confidence: 99%

Variable Pitch Active Disturbance Rejection Control of Wind Turbines Based on BP Neural Network PID

et al. 2020

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When wind speeds are above the rated speed of variable speed variable pitch wind turbines, pitch angles are changed to keep output powers and rotor speeds at their rated values. For wind turbines with nonlinear and complex structure, conventional PID variable pitch controller is difficult to achieve precise control. In this paper, a variable pitch controller combining back-propagation(BP) neural network with PID (BP-PID) is proposed. By real-time detecting the deviation of the rotor speeds, the BP neural network with self-learning and weighting coefficient correction capability is used to adjust the PID parameters online and further to achieve the optimal combination of the PID parameters. Considering various uncertain disturbances and parameter changes on the mechanical components of the wind turbines, an active disturbance rejection pitch controller of the wind turbines is designed based on BP-PID algorithm. Combined with a tracking differentiator, an extended state observer (ESO) is employed to observe the state and disturbance of the system. In addition, in order to compensate the BP-PID variable pitch controller, nonlinear state error feedback control laws are designed by configuring nonlinear structures according to the state deviation between the extended state observer and the tracking differentiator. The simulation results show that the variable pitch active disturbance rejection control (ADRC) based on BP-PID can effectively estimate the system states and disturbances. And the proposed controller has good dynamic performance and strong robustness. INDEX TERMS Wind turbine, pitch control, BP-PID, active disturbance rejection control, extended state observer.

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“…According to the equivalent circuit of the doubly-fed generator and the equations of the stator and rotor, the electromagnetic torque expression is as follows [12].…”

Section: Wind Turbine System Modellingmentioning

confidence: 99%

Variable Pitch Active Disturbance Rejection Control of Wind Turbines Based on BP Neural Network PID

et al. 2020

Self Cite

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“…Each particle will then be moved to another position while keeping track of its previous best position (p best ) and position of best performing particle in the swarm ( g best ). Based on the distance between the previous best position and global best position, the position and velocity of the particle in the swarm are then updated at the end of iteration using Equations (33) and (34)…”

Section: Teaching Learning Based Optimizationmentioning

confidence: 99%

“…The research work on the aerodynamical subsystem generally concerns the modeling of the wind turbine, which extracts the kinetic energy of wind and transferring it to the generator through a mechanical shaft. As per Betz's law, the extracted wind turbine power, 34 P t is expressed in Equation (1) and the wind turbine is modeled as in Figure 2, where, ρ is the air density, R is the radius of the swept area by turbine blades and v w is wind speed.

P_{t} = \frac{1}{2} italicρπ R^{2} {v_{w}}^{3} C_{p} ((),, λ, β)

…”

Section: System Description and Modelingmentioning

confidence: 99%

Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system

Pathak

Bhati

Gaur

2020

Int Trans Electr Energ Syst

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Objective To maintain the operation of a wind turbine at its maximum efficiency and for the extraction of maximum power from the wind. Method In this paper, at first, a novel application of fractional‐order nonlinear proportional‐integral‐derivative (FONPID) controller is proposed in the machine side converter (MSC) control loop of a 2 MW grid‐connected wind energy system. Then, the grid side converter (GSC) is controlled to ensure the unity power factor of the understudy system. To extract the maximum power from the wind, the application of the proposed controller is applied to a well‐established TSR MPPT control method, which compares optimal and actual values of rotor speed, and generated error is given to the proposed controller to vary the rotor speed accordingly. Moreover, improved MPPT performance offered by the proposed FONPID controller has also been compared to well‐established existing controllers based TSR MPPT methods. Furthermore, parameters of the proposed and existing controllers in the TSR MPPT control loop are also tuned using teaching‐learning based optimization (TLBO) and obtained performance is compared with the performance of particle swarm optimization (PSO) to show effectiveness. Results The generator speed offered by the proposed FONPID based TSR MPPT method effectively tracks its reference values far better than the extant controller based TSR MPPT method, whereas extant NPID has less overshoot as compared to the extant PID based TSR MPPT method. Conclusion To perform the effective operation of achieving MPP, q‐axis current of the stator has been controlled by the proposed FONPID based TSR MPPT method in such a style that rotor speed can be operated at its optimal value where the generator has the maximum power for given wind speed. DC‐link voltage is controlled at its rated value to guarantee the unity power factor operation using the GSC controller. FONPID based TSR MPPT method provides a better and robust MPPT operation than the PID, NPID, and FOPID controllers based TSR MPPT method. The performance has been assessed in terms of minimized fitness function value, steady‐state error, settling time, and percentage overshoot.

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“…Feedforward control is suitable for nonlinear systems [8,9,10]. Figure 1 shows a PID controller with feedforward compensate before an error is detected, resulting in a fast response and reduced error [11]. Controling frequency is posible according of two principle: the first principle is primary frequency control, when the power system load control regulates the frequency according to load changes.…”

Section: Introductionmentioning

confidence: 99%

Power control of a wind turbine using an electrohydraulically controlled active pitch control mechanism

Chiriţă¹,

Şefu²,

Baciu³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The paper presents the numerical simulation of a horizontal axis wind turbine and the electrohydraulic system that maintains the constant frequency of the alternating current generator regardless of its load or the variation of the wind speed. As a starting point, the turbine parameters such as speed, torque and power were determined in relation to the angle of attack of the blades. In order to keep the frequency of the alternating current generator constant, the solution that adjusts the angle of attack of the blades was chosen using the active pitch control mechanism. This mechanism is controlled by a PID type controller, by controlling a proportional directional control valve that feeds the 3 orbital hydraulic motors, which are coupled in series so that the angle of the three blades is identical.

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Feedforward Feedback Pitch Control for Wind Turbine Based on Feedback Linearization with Sliding Mode and Fuzzy PID Algorithm

Cited by 11 publications

References 34 publications

Variable Pitch Active Disturbance Rejection Control of Wind Turbines Based on BP Neural Network PID

Variable Pitch Active Disturbance Rejection Control of Wind Turbines Based on BP Neural Network PID

Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system

Power control of a wind turbine using an electrohydraulically controlled active pitch control mechanism

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