Alleviation of Wind Turbines Loads with a LQG Controller associated to Intelligent Micro Sensors

Abstract

This paper presents the first results of a research intelligent micro sensors in order to alleviate the fatigue loads project leaded by LIPSI-ESTIA concerning the use of intelligent experienced by the WTs blades. Only first simulation tests micro sensors to optimize the design of Wind Turbines (WTs) ee ren ed heW e. O nly ir simulation tests controllers. The sensors situated on the blades are above all used to alleviate WT fatigue loads in above rated wind speed operating hardware in the loop will be realized in the next months. area. Two LQG controllers are designed. One is associated to a This article describes the framework of the presented results sensor situated in the tower. The second controller is associated in section 2. Section 3 deals with the explanation about the to sensors placed at the end of the blades. Some simulations are LQG controllers developed until now. In section 4, simulation realized on a model of a two-bladed WT. They show that the second controller with sensors placed on the blades allow considerably alleviating WT fatigue loads.

Comparison of wind turbine LQG controllers using Individual Pitch Control to alleviate fatigue loads

“…To obtain the control model, the nonlinear model of the twobladed 400 [kW], 4 [rad/s] WT presented in [4] has been linearized around an operating points which corresponds to the high wind speeds zone, as explained later in this paper.…”

“…The LQG has shown to be effective in accommodating plant uncertainties in a systematic and straightforward way, and published works show that the LQG scheme may be used effectively in energy conversion control for wind generating systems [3]. An example is illustrated in [4], where the LQG is associated to intelligent micro-sensors placed on the blades and the WT tower to reduce loads in FL operations. An innovative strategy based on WS technology is proposed for use in offshore WT.…”

Full state feedback digital control of WECS with state estimation by stochastic modeling design

“…The LQG has shown to be effective in accommodating plant uncertainties in a systematic and straightforward way, and published works show that the LQG scheme may be used effectively in energy conversion control for wind generating systems [6], [7]. A practical implementation is reported by Lescher et al [8], where the LQG is incorporated in intelligent micro-sensors placed on the wind turbine blades and tower to monitor fatigue loads in the above-rated wind speed operating area.…”

Two LQRI based Blade Pitch Controls for Wind Turbines

“…Individual pitch control has been investigated by a number of researchers and shown to be beneficial [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. However, individual pitch control can reduce only the oscillation of blade bending moment, not its steady-state value.…”

“…Both pitch control algorithms use the LQR control technique with integral action (LQRI), and utilize Kalman filters to estimate system states and wind speed [5][6][7][8]19]. Compared to previous works, our collective pitch controller can control the rotor speed and collective, i.e., the steady-state value of, blade bending moments together to improve the trade-off between rotor speed regulation and load reduction, while the individual pitch controller reduces the fluctuating loads on the blades.…”