Output power leveling of wind turbine generator by pitch angle control using adaptive control method

Sakamoto, Ryosei; Senjyu, Tomonobu; Kinjo, Tadatsugu; Urasaki, Naomitsu; Funabashi, Toshihisa

doi:10.1109/icpst.2004.1460109

Cited by 42 publications

(19 citation statements)

References 5 publications

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“…On the other hand, below rated wind speed, the generator speed is fixed [4]. Rotors with adjustable pitch are often used in constant-speed machines to provide turbine power control better than the one achievable with blade stall [5]. Therefore, blade pitching can be regulated to provide constant power above rated wind speed.…”

Section: Introductionmentioning

confidence: 99%

Application of a Data-Driven Fuzzy Control Design to a Wind Turbine Benchmark Model

Simani

2012

Advances in Fuzzy Systems

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In general, the modelling of wind turbines is a challenging task, since they are complex dynamic systems, whose aerodynamics are nonlinear and unsteady. Accurate models should contain many degrees of freedom, and their control algorithm design must account for these complexities. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy, mainly when they have to be implemented in real-time applications. The first contribution of this work consists of providing an application example of the design and testing through simulations, of a data-driven fuzzy wind turbine control. In particular, the strategy is based on fuzzy modelling and identification approaches to model-based control design. Fuzzy modelling and identification can represent an alternative for developing experimental models of complex systems, directly derived directly from measured input-output data without detailed system assumptions. Regarding the controller design, this paper suggests again a fuzzy control approach for the adjustment of both the wind turbine blade pitch angle and the generator torque. The effectiveness of the proposed strategies is assessed on the data sequences acquired from the considered wind turbine benchmark. Several experiments provide the evidence of the advantages of the proposed regulator with respect to different control methods.

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Section: Introductionmentioning

confidence: 99%

Application of a Data-Driven Fuzzy Control Design to a Wind Turbine Benchmark Model

Simani

2012

Advances in Fuzzy Systems

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“…0 is always maintained for any je(0)j < d. Now, from Eqs. (11) and (13), it can be concluded that the asymptotic stability of the system is established by the well-known Barbalet's lemma, namely, C ! C Ã as t !…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…7,8 Later, modified DTC approaches were developed. Other control methods include PI-based control, 9 non-linear and adaptive control, 10,11 yaw control, [12][13][14] pitch control, [15][16][17][18] and inverter firing angle control. 19 Although several research papers on wind turbine control have been published, [20][21][22] few have addressed the issue of power conversion efficiency directly.…”

Section: Introductionmentioning

confidence: 99%

A bio-inspired approach to enhancing wind power conversion

Song

Cai

2012

Journal of Renewable and Sustainable Energy

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The primary objective of this investigation is to develop a new control method for wind turbines that can increase their wind energy capture efficiency. Since the resultant wind turbine system dynamics are profoundly nonlinear and coupled with significant uncertainties, traditional model-based control is found to be not only structurally complex but also computationally expensive. Here, we explore two sets of control algorithms to enhance wind to electrical energy conversion. The first accounts for system nonlinearities and external disturbances by integrating variable structure control with adaptive control. The second accommodates the nonlinearities arising from rotor aerodynamics and pitch (actuation) dynamics, as well as external disturbances, through a method inspired by a 1st order human memory/learning model. The second method allows direct maximum power coefficient tracking for winds under the rated speed and ensures rated power output for winds over the rated speed. Basically, it uses the system current and most recent memorized responses, together with past control experience, to generate new control actions. Both rotor dynamics and actuation (pitch) dynamics are reflected indirectly through the observed/measured system response at each instant, and are embedded within the control mechanism. Thus, there is no need for detailed information on the system model or system parameters in the control's design and implementation. The efficacy of both proposed approaches is analyzed through numerical simulations.

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“…Fig.4. The system actually has nonlinear characteristics, but it is able to be described by first-order lag system [4] . The output command of pitch angle control system is limited from 10°to 90°.…”

Section: Pitch Angle Control Systemmentioning

confidence: 99%

“…The Fuzzy-PID pitch angle control designed in [3] has improved the system performance, but the random component has not been considered in the wind speed model. In [4], the minimum variance control (MVC) is used to design pitch angle control for SCIG and the desired effect is obtained. In [5], the generalized predictive control (GPC) is applied by using the average wind speed and standard deviation of wind speed, GPC control is also used to achieve the control strategy in all operating regions.…”

Section: Introductionmentioning

confidence: 99%

The sliding mode pitch angle controller design for squirrel-cage induction generator wind power generation system

Xiaowei

Yang

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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The new pitch angle controller is designed for squirrel-cage induction generator (SCIG) wind power generation system by using sliding mode control method. Firstly, wind turbine generation (WTG) model is established by using the state variables of angular speed of windmill rotor, wind speed, pitch angle and so on. Secondly, the sliding mode pitch angle controller is designed making use of the constant reaching law which keep the output power of WTG at the rated power. Then, the proposed method is compared with traditional PID pitch angle controller via matlab software. The simulation results show that the system has more rapid and robust response and little deviations.

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Output power leveling of wind turbine generator by pitch angle control using adaptive control method

Cited by 42 publications

References 5 publications

Application of a Data-Driven Fuzzy Control Design to a Wind Turbine Benchmark Model

Application of a Data-Driven Fuzzy Control Design to a Wind Turbine Benchmark Model

A bio-inspired approach to enhancing wind power conversion

The sliding mode pitch angle controller design for squirrel-cage induction generator wind power generation system

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