Output Power Leveling of Wind Turbine Generator for All Operating Regions by Pitch Angle Control

Senjyu, Tomonobu; Sakamoto, Ryosei; Urasaki, Naomitsu; Funabashi, Toshihisa; Fujita, Hideki; Sekine, Hideomi

doi:10.1109/tec.2006.874253

Cited by 415 publications

(238 citation statements)

References 15 publications

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“…Especially, when the wind turbine works above the rated wind speed, the pitch control is used to reduce the overload phenomenon on the mechanical and electrical parts of the unit, see [1,2,3,4]. Also, the volatility of the wind speed can lead to the large range variety of operating points, while the turbulent wind speed can lead to the extra fatigue loads and output power ripple, which will cause a significant negative impact on the wind turbine mechanical side and the stability of the power grid [5]. Therefore, the control objective is to change the wind energy utilization coefficient of wind turbine through using the pitch controller, to stabilize the output power near the power rating adapting to the large range variety of operating points, restrain the wind disturbance and reduce the fatigue loads [6].…”

Section: Introductionmentioning

confidence: 99%

MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

Lin

Liu

et al. 2017

International Journal of Control

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Abstract-This paper proposes a Markovian jump model and the corresponding H 2 /H ∞ control strategy for the wind turbine driven by the stochastic switching wind speed, which can be used to regulate the generator speed in order to harvest the rated power while reducing the fatigue loads on the mechanical side of wind turbine. Through sampling the low-frequency wind speed data into separate intervals, the stochastic characteristic of the steady wind speed can be represented as a Markov process, while the high-frequency wind speed in the each interval is regarded as the disturbance input. Then, the traditional operating points of wind turbine can be divided into separate subregions correspondingly, where the model parameters and the control mode can be fixed in each mode. Then, the mixed H 2 /H ∞ control problem is discussed for such a class of Markovian jump wind turbine working above the rated wind speed to guarantee both the disturbance rejection and the mechanical loads objectives, which can reduce the power volatility and the generator torque fluctuation of the whole transmission mechanism efficiently. Simulation results for a 2 MW wind turbine show the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

Lin

Liu

et al. 2017

International Journal of Control

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“…The pitch angle is controlled to regulate the wind turbine (WT) output power following a smoothened power reference; however, this method increases system complexity, and the WT might not capture the maximum power of the wind [6]. Recently, the utilization of energy storage systems (ESSs) as an energy buffer to compensate for wind power fluctuation has been proposed and investigated in several literatures [7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wind Power Dispatch to Minimize Energy Storage System Capacity

Nguyen¹,

Lee²

2014

Journal of Electrical Engineering and Technology

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-By combining a wind turbine with an energy storage system (ESS), we are able to attenuate the intermittent wind power characteristic making the power derived from a wind farm dispatchable. This paper evaluates the influence of the phase delay of the low-pass filter in the conventional smoothing power control on the ESS capacity; longer phase delays require a larger ESS capacity. In order to eliminate the effect of the phase delay, we optimize the power dispatch using a zero-phase low-pass filter that results in a non-delayed response in the power dispatch. The proposed power dispatching method significantly minimizes the ESS capacity. In addition, the zero-phase lowpass filter, which is a symmetrical forward-reverse finite impulse response type, is designed simply with a small number of coefficients. Therefore, the proposed dispatching method is not only optimal, but can also be feasibly applied to real wind farms. The efficacy of the proposed dispatching method is verified by integrating a 3 MW wind turbine into the grid using wind data measured on Jeju Island.

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“…This allows wind farms to further improve their generation predictability. Since almost all modern wind turbines are able to regulate their blade pitch angles, it is possible to change the existing pitch control algorithms and include more grid friendly output power regulation schemes [1]. In variable speed wind turbines (VSWTs), controlling the rotor speed and changing the power transferred through the converters also allow output power regulation.…”

Section: Introductionmentioning

confidence: 99%

Dispatch strategy to minimise uncertainty in wind power generation in the Australian national electricity market

Wickramasinghe

Meegahapola

Agalgaonkar

et al. 2014

2014 Australasian Universities Power Engineering Conference (AUPEC)

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Abstract2014 ACPE. With increased penetration of wind power, scheduling generators to meet the forecast demand of a power system is becoming an increasingly challenging task for the system operators. Uncertainty associated with the generation expected from wind plants adds to the load demand uncertainty, making it necessary to retain additional reserves to maintain the balance between demand and supply of power. In the Australian national electricity market (NEM), sophisticated wind forecasting techniques are employed to reduce the uncertainty in wind generation. Despite being able to project the contribution of wind power to a reasonable accuracy, wind power plants are currently not considered to be dispatchable resources in the NEM. This paper discusses the possibility of assigning firm generation responsibilities to wind plants, especially in a dynamic electricity markets as the NEM, where the generation scheduling happens between five to ten minutes ahead of actual dispatch. Abstract-With increased penetration of wind power, scheduling generators to meet the forecast demand of a power system is becoming an increasingly challenging task for the system operators. Uncertainty associated with the generation expected from wind plants adds to the load demand uncertainty, making it necessary to retain additional reserves to maintain the balance between demand and supply of power. In the Australian national electricity market (NEM), sophisticated wind forecasting techniques are employed to reduce the uncertainty in wind generation. Despite being able to project the contribution of wind power to a reasonable accuracy, wind power plants are currently not considered to be dispatchable resources in the NEM. This paper discusses the possibility of assigning firm generation responsibilities to wind plants, especially in a dynamic electricity markets as the NEM, where the generation scheduling happens between five to ten minutes ahead of actual dispatch.

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Output Power Leveling of Wind Turbine Generator for All Operating Regions by Pitch Angle Control

Cited by 415 publications

References 15 publications

MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

Optimization of Wind Power Dispatch to Minimize Energy Storage System Capacity

Dispatch strategy to minimise uncertainty in wind power generation in the Australian national electricity market

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Output Power Leveling of Wind Turbine Generator for All Operating Regions by Pitch Angle Control

Cited by 415 publications

References 15 publications

MixedH2/H∞pitch control of wind turbine with a Markovian jump model

MixedH2/H∞pitch control of wind turbine with a Markovian jump model

Optimization of Wind Power Dispatch to Minimize Energy Storage System Capacity

Dispatch strategy to minimise uncertainty in wind power generation in the Australian national electricity market

Contact Info

Product

Resources

About

MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model