Nevena Perisic

Load Identification of Offshore Platform for Fatigue Life Estimation

The lifetime of an offshore platform is typically governed by accumulated fatigue damage. Thus, the load time history is an essential parameter for prediction of the lifetime of the structure and its components. Consequently, monitoring of structural loads is of special importance in relation to re-assessment of offshore platforms. Structural monitoring systems (SMS’s) on offshore structures typically consist of a set of sensors such as strain gauges, accelerometers, wave radars and GPS’s, however direct measuring of the actual loading is usually not feasible. One approach is to measure the loads indirectly by monitoring of the available dynamic responses of the structure. This work investigates the possibility for using an economically beneficial, model-based load estimation algorithm for indirect measuring of the loading forces acting on the offshore structure. The algorithm is based on the reduced order model of the structure and the discrete Kalman filter which recursively estimates unknown states of the system in real time. As a test-case, the algorithm is designed to estimate the equivalent total loading forces of the structure. The loads are estimated from noised displacement measurements of a single location on the topside of the offshore structure. The method is validated using simulated data for two wave loading cases: regular and irregular wave loadings.

Cost-Effective Load Monitoring Methods for Fatigue Life Estimation of Offshore Platform

The lifetime of offshore structures are ruled by accumulated fatigue damage so structural load monitoring is of special importance for re-assessment of offshore platforms. However, direct measuring of the actual loading is usually not feasible due to sensor limitations. An alternative approach to direct load measuring is load estimation from the limited number of measurements of dynamic responses of the structure. This paper presents two, conceptually different, model based, methods for load estimation in time domain: modal expansion method and Kalman filter based method. The Kalman filter based method uses the reduced FEM and provides estimations of limited number of signals, where the modal expansion method expands the mode shape vectors to estimate the displacements and stresses at all unmeasured locations. This paper is a part of ongoing development of methods for linear and nonlinear system identification, FEM updating, wave load calibration and lifetime prediction of offshore structures.Copyright

Blade Bearing Friction Estimation of Operating Wind Turbines

Blade root bearing on a wind turbine (WTG) enables pitching of blades for power control and rotor braking and is a WTG critical component. As the size of modern WTGs is constantly increasing, this leads to relatively less rigid bearings, more sensitive to deformations, thus WTG operational reliability can be increased by continuous monitoring of blade bearing. High blade bearing friction is undesirable, as it may be associated with excessive heating of the surfaces, damage and/or inefficient operation. Thus, continuous observation of bearing friction level is crucial for blade bearing health monitoring systems. A novel algorithm for online monitoring of bearing friction level is developed combining physical knowledge about pitch system dynamics with state estimator, i.e. observer theory and signal processing assuming realistic sensor availability. Results show estimation of bearing friction torque required for condition monitoring, and a corresponding indicator signal which represents estimation of a relative friction coefficient. All data used in this work have been calculated using the aeroelastic code, LACflex applying a model of a generic multi-MW WTG.

Low-Cost Tower Root Fatigue Load Estimation for Structural Health Monitoring of Grouted Connections in Offshore Wind Turbines

The sinking of wind turbines (WTs) with monopile foundations is one of the major issues in the offshore wind industry nowadays. Dynamic wind and wave loads act on the WTs causing vibrations of the structure. However, grouted connections in the monopiled WTs are not designed well enough to transfer bending moments from the wind loading. When the load capacity of the grouted connection is reached, stress cracks appear in the grout causing transition piece to slide down. Direct measuring of the fatigue load, called the tower bending moment, causing fatigue failures and sinking of the WTs is expensive and practically unfeasible. This paper suggests a low-cost, model-based algorithm for indirect measuring of the tower bending moments from the WT dynamic response measurements. The bending moment is estimated recursively using well-known Kalman filter theory. The method is validated using WT simulated data, assuming different measurement noise levels.

Identification of Time-Varying Nonlinear Systems Using Differential Evolution Algorithm

Online monitoring of modal and physical parameters which change due to damage progression and aging of mechanical and structural systems is important for the condition and health monitoring of these systems. Usually, only the limited number of imperfect, noisy system state measurements is available, thus identification of time-varying systems with nonlinearities can be a very challenging task. In order to avoid conventional least squares and gradient identification methods which require uni-modal and double differentiable objective functions, this work proposes a modified differential evolution (DE) algorithm for the identification of time-varying systems. DE is an evolutionary optimisation method developed to perform direct search in a continuous space without requiring any derivative estimation. DE is modified so that the objective function changes with time to account for the continuing inclusion of new data within an error metric. This paper presents results of identification of a time-varying SDOF system with Coulomb friction using simulated noise-free and noisy data for the case of time-varying friction coefficient, stiffness and damping. The obtained results are promising and the focus of the further work will be on the convergence study with respect to parameters of DE and on applying the method to experimental data.