Pitch Angle Control and Wind Speed Prediction Method Using Inverse Input-Output Relation of a Wind Generation System

Hyun, Seung-Ho; Wang, Jialong

doi:10.5370/jeet.2013.8.5.1040

Cited by 3 publications

(5 citation statements)

References 30 publications

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“…The value of electricity that turbine is capable to produce depends on the rotor speed and wind speed . The WT mechanical power can be expressed using equation (17):

P = 0.5 ρ A C_{p} ((),, λ, β) V_{w}^{3}

λ = \frac{W_{m} R}{V_{w}}

where P, ρ , A, V w , W m and R are power, air density, rotor swept area of the wind turbine, wind speed in m/s, rotor speed in rad/s and radius of turbine, respectively.…”

Section: Wind Turbine System Configurationmentioning

confidence: 99%

Section: Wind Turbine System Configurationmentioning

confidence: 99%

“…The value of electricity that turbine is capable to produce depends on the rotor speed and wind speed [45,46]. The WT mechanical power can be expressed using equation (17):…”

Section: Wind Turbine and Pmsg Modelingmentioning

confidence: 99%

“…The pitch angle starts to increment from the zero value which wind attach angle to the blades will be incremented, thereby leading to decrement of aerodynamic power and decreasing the turbine output power. Alongside, the second signal is derived from anemometer nacelle [45,46]. The responses of fuzzy controller will be faster, smoother and more stable while wind speed is applied as an input signal comparing to the rotor speed and reactive power in wind turbines [32][33][34].…”

Section: Pitch Angle Based On Fuzzy Controllermentioning

confidence: 99%

See 3 more Smart Citations

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

Rezvani

Izadbakhsh

Gandomkar

2015

Int J Numerical Modelling

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The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind speeds is proposed using pitch angle control technic by fuzzy logic. To track the maximum power point (MPP) in the photovoltaic (PV), the proposed ANN is trained by the genetic algorithm (GA). In other word, the data are optimized by GA, and then these optimum values are used in ANN. The simulation results show that the ANN‐GA in comparison with the conventional algorithms with high accuracy can track the peak power point under different insolation conditions and meet the load demand with less fluctuation around the MPP; also it can increase convergence speed to achieve MPP. Moreover, pitch angle controller based on fuzzy logic with wind speed and active power as inputs that have faster responses which leads to have flatter power curves enhances the dynamic responses of wind turbine. The models are developed and applied in Matlab/Simulink. Copyright © 2015 John Wiley & Sons, Ltd.

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“…The value of electricity that turbine is capable to produce depends on the rotor speed and wind speed . The WT mechanical power can be expressed using equation (17):

P = 0.5 ρ A C_{p} ((),, λ, β) V_{w}^{3}

λ = \frac{W_{m} R}{V_{w}}

where P, ρ , A, V w , W m and R are power, air density, rotor swept area of the wind turbine, wind speed in m/s, rotor speed in rad/s and radius of turbine, respectively.…”

Section: Wind Turbine System Configurationmentioning

confidence: 99%

Section: Wind Turbine System Configurationmentioning

confidence: 99%

“…The value of electricity that turbine is capable to produce depends on the rotor speed and wind speed [45,46]. The WT mechanical power can be expressed using equation (17):…”

Section: Wind Turbine and Pmsg Modelingmentioning

confidence: 99%

Section: Pitch Angle Based On Fuzzy Controllermentioning

confidence: 99%

See 2 more Smart Citations

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

Rezvani

Izadbakhsh

Gandomkar

2015

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…Minimization of the output power fluctuation can be realized by either controllers without additional apparatus or utilization of additional devices. In [8] and [9], a strategy using the pitch angle control system has been presented, where no additional device is required. However, all available energy extracted from the wind is not captured due to an increase of the pitch angle.…”

Section: Introductionmentioning

confidence: 99%

Power Smoothening Control of Wind Farms Based on Inertial Effect of Wind Turbine Systems

Nguyễn¹,

Lee²,

Kang³

2014

Journal of Electrical Engineering and Technology

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-This paper proposes a novel strategy for attenuating the output power fluctuation of the wind farm (WF) in a range of tens of seconds delivered to the grid, where the kinetic energy caused by the large inertia of the wind turbine systems is utilized. A control scheme of the two-level structure is applied to control the wind farm, which consists of a supervisory control of the wind farm and individual wind turbine controls. The supervisory control generates the output power reference of the wind farm, which is filtered out from the available power extracted from the wind by a low-pass filter (LPF). A lead-lag compensator is used for compensating for the phase delay of the output power reference compared with the available power. By this control strategy, when the reference power is lower than the maximum available power, some of individual wind turbines are operated in the storing mode of the kinetic energy by increasing the turbine speeds. Then, these individual wind turbines release the kinetic power by reducing the turbine speed, when the power command is higher than the available power. In addition, the pitch angle control systems of the wind turbines are also employed to limit the turbine speed not higher than the limitation value during the storing mode of kinetic energy. For coordinating the de-rated operation of the WT and the storing or releasing modes of the kinetic energy, the output power fluctuations are reduced by about 20%. The PSCAD/EMTDC simulations have been carried out for a 10-MW wind farm equipped with the permanent-magnet synchronous generator (PMSG) to verify the validity of the proposed method.

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Control of a Wind Turbine Emulator Based on Variable Pitch Angle with a Self-Excited Induction Generator

Boutahiri,

Nouaiti,

Mesbahi

et al. 2024

2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

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Pitch Angle Control and Wind Speed Prediction Method Using Inverse Input-Output Relation of a Wind Generation System

Cited by 3 publications

References 30 publications

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

Power Smoothening Control of Wind Farms Based on Inertial Effect of Wind Turbine Systems

Control of a Wind Turbine Emulator Based on Variable Pitch Angle with a Self-Excited Induction Generator

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