Output power levelling for DFIG wind turbine system using intelligent pitch angle control

Hosseini, Ehsan; Shahgholian, Ghazanfar

doi:10.1080/00051144.2018.1455017

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…To obtain the wind acceleration, the following first order model is utilized []

{\overset{ξ}{true}}_{1} = - \frac{1}{τ_{v}} false(ξ_{1} - \overset{v}{true} false)

where τ v > 0 denotes delay time constant,

\overset{v}{true}

is the mean wind speed over an appropriate time period. Model is derived from the Kaimal wind spectrum []. Let

ξ_{2} = {\overset{ξ}{true}}_{1}

denote the estimation of the wind acceleration

\overset{v}{true}

.…”

Section: Controller Methodologymentioning

confidence: 99%

See 1 more Smart Citation

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

Jiao

Yang

Fu³

et al. 2018

Asian Journal of Control

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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.

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“…To obtain the wind acceleration, the following first order model is utilized []

{\overset{ξ}{true}}_{1} = - \frac{1}{τ_{v}} false(ξ_{1} - \overset{v}{true} false)

where τ v > 0 denotes delay time constant,

\overset{v}{true}

is the mean wind speed over an appropriate time period. Model is derived from the Kaimal wind spectrum []. Let

ξ_{2} = {\overset{ξ}{true}}_{1}

denote the estimation of the wind acceleration

\overset{v}{true}

.…”

Section: Controller Methodologymentioning

confidence: 99%

“…where v > 0 denotes delay time constant,v is the mean wind speed over an appropriate time period. Model (36) is derived from the Kaimal wind spectrum [26]. Let 2 =̇1 denote the estimation of the wind acceleratioṅ v. Since the wind speed is bounded because of its physical nature,v is bounded.…”

Section: Controller Designmentioning

confidence: 99%

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

Jiao

Yang

Fu³

et al. 2018

Asian Journal of Control

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show abstract

“…From the above equations, it could be ascertain that for different pitch angles of β P , the blade tip speed ratio to the wind speed can be obtained. The power curve with power limitation in the pitch angle sector [53] is shown in Fig. 2a.…”

Section: Dfig Wind Turbine and Its Modellingmentioning

confidence: 99%

Improving the transient performance of DFIG wind turbine using pitch angle controller low pass filter timing and network side connected damper circuitry

Okedu

2020

IET Renewable Power Generation

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In wind turbine systems, pitch angle controllers are employed to reduce the aerodynamic power gained during high wind speeds. On the other hand, passive filters are usually used to mitigate disturbances in grid-connected voltage source converters (VSCs). To avoid the risk of instability in the grid-connected VSC, as a result of resonance in the capacitive and inductive components, it is necessary to consider damping. In this study, the low pass filter timing constant of the pitch angle controller of the doubly-fed induction generator (DFIG) wind turbine was varied considering different time constants. The best timing response of the low pass filter was further used to analyse different filter topologies, in addition to a new control strategy that uses two trap passive filters with shunt resistor-capacitor as an active damper, in augmenting the DFIG wind turbine during grid fault. Simulation studies in PSCAD/EMTDC were carried out to compare the performance of the proposed scheme with some other conventional filter solutions for the DFIG wind generator, during a severe bolted three-phase to ground fault. The simulation results demonstrate the improved performance and faster recovery of the wind generator variables after the fault considering the proposed filter scheme.

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“…Among various kinds of renewable energy sources, wind energy entices the attention of researchers as it offers clean energy, rapid enlargement of power generation site, and increased competitory with other electrical power generation sources. 1 Due to the fast cumulative installation capacity of the wind energy conversion system (WECS), several topologies of WECSs have been designed and investigated in the literature. These WECSs have been categorized into three types; fixed-speed, semi-variable speed, and variable-speed systems.…”

Section: Introductionmentioning

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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Output power levelling for DFIG wind turbine system using intelligent pitch angle control

Cited by 24 publications

References 33 publications

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

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

Improving the transient performance of DFIG wind turbine using pitch angle controller low pass filter timing and network side connected damper circuitry

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

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