Abstract: Future prospects of the global wind industry are very promising: even on a conventional scenario the total wind power installed worldwide is projected to more than double from 74 GW by end of 2006 to 160 GW by 2012. The main challenge is wind stochasticity that impacts on both power quality and drive-train fatigue from cyclic loading for a wind energy conversion system (WECS). To investigate amelioration of these problems, the approach in this study involves firstly, modeling: the wind speed is generated by an autoregressive moving average (ARMA) model, while the turbine, gearbox, and generator subsystems are represented with a spring-mass-damper mathematical equivalent. Secondly, an advanced intelligent control paradigm for optimality and reliability is formulated based on the models. The control strategy—involving design of a linear quadratic Gaussian (LQG) to damp these undesired oscillations—is applied to the detailed performability model. A pitch controller prevents rotor overspeed by ensuring the maximum power constraint. Computer simulations reveal that load reduction through ‘intelligent’ control systems becomes more attractive compared with designing mechanical systems to cope with large loads.

Abstract: Operation of variable speed wind turbine generator (WTG) in the above-rated region characterized by high turbulence intensities demands a trade-off between two performance metrics: maximization of energy harvested from the wind and minimization of damage caused by mechanical fatigue. This paper presents a learning adaptive controller for output power leveling and decrementing cyclic loads on the drive train. The proposed controller incorporates a linear quadratic Gaussian (LQG) augmented by a neurocontroller (NC) and regulates rotational speed by specifying the demanded generator torque. Pitch control ensures rated power output. A second-order model and a stochastic wind field model are used in the analysis. The LQG is used as a basis upon which the performance of the proposed paradigm in the trade-off studies is assessed. Simulation results indicate the proposed control scheme effectively harmonizes the relation between rotor speed and the highly turbulent wind speed thereby regulating shaft moments and maintaining rated power.

Abstract: Operation of wind turbine generator (WTG) systems in the above-rated region characterised by high wind turbulence intensities invariably induces fatigue stresses on the drive train components. This demands a trade-off between two performance metrics: maximisation of energy harvested from the wind and minimisation of the damage caused by mechanical fatigue. A learning adaptive controller in the form of a self-tuning regulator (STR) for output power levelling and decrementing fatigue loads is presented. The STR incorporates a hybrid controller of a linear quadratic Gaussian (LQG), neurocontroller and a linear parameter estimator (LPE). The main control objective is to regulate the relationship between rotational speed and wind speed by controlling the generator torque and further, the rotational speed. A pitch actuator ensures system operation geared toward maintaining output at rated power. A second-order model and a stochastic wind field model are used to systematically analyse the dynamical relationship between the WTG subsystems. The LQG is used as a basis upon which the performance of the proposed method in the trade-off studies is assessed. Simulation results indicate the proposed control scheme captures the performance and critical reliability loci thereby ensuring the wind turbine operates optimally in mechanically harmless conditions.

Abstract: Probabilistic paradigms for wind turbine controller design have been gaining attention. Motivation derives from the need to replace outdated empirical-based designs with more physically relevant models. This paper proposes an adaptive controller in the form of a linear quadratic Gaussian (LQG) for control of a stall-regulated, variable speed wind turbine generator (WTG). In the control scheme, the strategy is twofold: maximization of energy captured from the wind and minimization of the damage caused by mechanical fatigue due to variation of torque peaks generated by wind gusts. Estimated aerodynamic torque and rotational speed are used to determine the most favorable control strategy to stabilize the plant at all operating points (OPs). The performance of the proposed controller is compared with the classical proportional-integral-derivative (PID) controller. The LQG is seen to be significantly more efficient especially in the alleviation of high aerodynamic torque variations and hence mechanical stresses on the plant drive train. Simulation results validate the effectiveness of the proposed method.

Abstract: Although variable-speed operation can reduce the impact of transient wind gusts and subsequent component fatigue, this is still an unknown factor that must now be quantified. Reduction in drive-train stresses caused by fatigue loads in high wind turbulence is fundamental to realizing both output power leveling and long service life for a wind turbine generator (WTG). This paper presents an evolutionary controller comprising a linear quadratic Gaussian (LQG) and neurocontroller acting in tandem to effect optimal performance under high turbulence intensities, for a variable-speed, fixed-pitch WTG. The control objectives are maximum energy conversion and reduction in mechanical stresses on the system components. The proposed paradigm utilizes generator torque in controlling the rotor speed in relation to the highly turbulent wind speed, thereby ensuring the extracted aerodynamic power is maintained at a constant value, while shaft moments are regulated. The performance of the proposed controller is compared with that of the LQG and it is found that the former is more efficient in maintaining rated power, minimizing shaft torque variations, and shows robustness to parameter variations.

Abstract: In order to perform maximum power point tracking control of wind generation system, it is necessary to drive windmill at an optimal rotor speed. For that purpose, a rotor position and a wind velocity sensors become indispensable. However, from the aspect of reliability and increase in cost, rotor position sensor and wind velocity sensor are not usually preferred. Hence, wind velocity and position sensorless operating method for wind generation system using observer is proposed in this paper. Moreover, improving the efficiency of the permanent magnet synchronous generator is also performed by optimizing d-axis current using the Powell method.