This paper aims at the blade root moment sensor fault detection and isolation issue for three-bladed wind turbines with horizontal axis. The underlying problem is crucial to the successful application of the individual pitch control system, which plays a key role for reducing the blade loads of large offshore wind turbines. In this paper, a wind turbine model is built based on the closed loop identification technique, where the wind dynamics is included. The fault detection issue is investigated based on the residuals generated by dual Kalman filters. Both additive faults and multiplicative faults are considered in this paper. For the additive fault case, the mean value change detection of the residuals and the generalized likelihood ratio test are utilized respectively. For multiplicative faults, they are handled via the variance change detection of the residuals. The fault isolation issue is proceeded with the help of dual sensor redundancy. Simulation results show that the proposed approach can be successfully applied to the underlying issue.
Further improvements in the cost-effectiveness of wind turbines drives designers towards larger, lighter, more flexiblestructures in which more intelligent control systems play an important part in actively reducing the applied structural loads, avoiding the need for wind turbines to simply withstand the full force of the applied loads through the use of stronger, heavier and therefore more expensive structures. Controller research within the UPWIND project has been aimed at further developing such control strategies and ensuring that new, often larger and innovative turbines can be designed to use these techniques from the start. For this to be possible, it is important to build up full confidence in the effectiveness and the reliability of these strategies in all situations. To this end, the work reported in this paper covers several different aspects: full-scalefield testing to build confidence in the effectiveness of advanced control strategies; further development of advanced control strategies to prevent unwelcome side effects in any of the load cases that have to be considered during the design; the possibility of blades employing dual-pitch control; development of load estimation techniques that can reduce reliance on additional sensors that would otherwise be required; investigating the potential of light detection and ranging assisted feed-forward pitch control to mitigate extreme and fatigue loads; using system identification methods to improve controller tuning. The detailed results of the work presented in this paper are available in the published reports of the Control Systems work package of the UPWIND project. These reports also cover other results of the work package, which are not reported here, such as control during network faults such as voltage dips, voltage control at the point of connection to the network and gradual cut-out of wind turbines to improve output predictability in high winds. A summary report is also available
This paper focuses on the problem of extreme wind gust and direction change recognition (EG&DR) and control (EEC). An extreme wind gust with direction change can lead to large loads on the turbine (causing fatigue) and unnecessary turbine shutdowns by the supervisory system caused by rotor overspeed. The proposed EG&DR algorithm is based on a non-linear observer (extended Kalman fi lter) that estimates the oblique wind infl ow angle and the blade effective wind speed signals, which are then used by a detection algorithm (cumulative sum test) to recognize extreme events. The nonlinear observer requires that blade root bending moments measurements (in-plane and out-of-plane) are available. Once an extreme event is detected, an EEC algorithm is activated that: (i) tries to prevent the rotor speed from exceeding the overspeed limit by fast collective blade pitching; and (ii) reduces 1p blade loads by means of individual pitch control algorithm, designed in an H ∞ optimal control setting. The method is demonstrated on a complex non-linear test turbine model.
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