The vibration behavior of wind turbine substructures is mainly dominated by their first few vibration modes because wind turbines operate at low rotational speeds. In this study, 13 degrees of freedom (DOF) model of a wind turbine is derived considering fundamental vibration modes of the tower and blades which are modelled as rigid beams with torsional springs attached at their root. Linear equations of motion (EOM) governing the structural behavior of wind turbines are derived by assuming small amplitude vibrations. This model is used to study the coupling between the structural and aerodynamic behavior of NREL 5 MWmodel wind turbine. Aeroelastic natural frequencies of the current model are compared with the results obtained from the finite element model of this wind turbine. Quasi-steady aerodynamic loads are calculated considering wind velocity changes due to height and tower shadow effects. In this study, vibration responses are simulated at various wind velocities. The derived 13 DOF simplified model of the wind turbine enables to simulate the influence ofchange in parameters and operating conditions on vibration behavior with less computational effort. Besides that, the results of the simplified models can be interpreted with much ease.

Wind turbines installed in cold climate sites accumulate ice on their structures. Icing of the rotor blades reduces turbine power output and increases loads, vibrations, noise, and safety risks due to the potential ice throw. Ice accumulation increases the mass distribution of the blade, while changes in the aerofoil shapes affect its aerodynamic behavior. Thus, the structural and aerodynamic changes due to icing affect the modal behavior of wind turbine blades. In this study, aeroelastic equations of the wind turbine blade vibrations are derived to analyze modal behavior of the Tjaereborg 2 MW wind turbine blade with ice. Structural vibrations of the blade are coupled with a Beddoes-Leishman unsteady attached flow aerodynamics model and the resulting aeroelastic equations are analyzed using the finite element method (FEM). A linearly increasing ice mass distribution is considered from the blade root to half-length and thereafter constant ice mass distribution to the blade tip, as defined by Germanischer Lloyd (GL) for the certification of wind turbines. Both structural and aerodynamic properties of the iced blades are evaluated and used to determine their influence on aeroelastic natural frequencies and damping factors. Blade natural frequencies reduce with ice mass and the amount of reduction in frequencies depends on how the ice mass is distributed along the blade length; but the reduction in damping factors depends on the ice shape. The variations in the natural frequencies of the iced blades with wind velocities are negligible; however, the damping factors change with wind velocity and become negative at some wind velocities. This study shows that the aerodynamic changes in the iced blade can cause violent vibrations within the operating wind velocity range of this turbine.

Structures vibrate with their natural frequencies when disturbed from their equilibrium position. These frequencies reduce when an additional mass accumulates on their structures, like ice accumulation on wind turbines installed in cold climate sites. The added mass has two features: the location and quantity of mass. Natural frequencies of the structure reduce differently depending on these two features of the added mass. In this work, a technique based on an artificial neural network (ANN) model is proposed to identify added mass by training the neural network with a dataset of natural frequencies of the structure calculated using different quantities of the added mass at different locations on the structure. The proposed method is demonstrated on a non-rotating beam model fixed at one end. The length of the beam is divided into three zones in which different added masses are considered, and its natural frequencies are calculated using a finite element model of the beam. ANN is trained with this dataset of natural frequencies of the beam as an input and corresponding added masses used in the calculations as an output. ANN approximates the non-linear relationship between these inputs and outputs. An experimental setup of the cantilever beam is fabricated, and experimental modal analysis is carried out considering a few added masses on the beam. The frequencies estimated in the experiments are given as an input to the trained ANN model, and the identified masses are compared against the actual masses used in the experiments. These masses are identified with an error that varies with the location and the quantity of added mass. The reason for these errors can be attributed to the unaccounted stiffness variation in the beam model due to the added mass while generating the dataset for training the neural network. Therefore, the added masses are roughly estimated. At the end of the paper, an application of the current technique for detecting ice mass on a wind turbine blade is studied. A neural network model is designed and trained with a dataset of natural frequencies calculated using the finite element model of the blade considering different ice masses. The trained network model is tested to identify ice masses in four test cases that considers random mass distributions along the blade. The neural network model is able to roughly estimate ice masses, and the error reduces with increasing ice mass on the blade.

This work demonstrates a technique to identify information about the ice mass accumulation on wind turbine blades using its natural frequencies, and these frequencies reduce differently depending on the spatial distribution of ice mass along the blade length. An explicit relation to the natural frequencies of a 1-kW wind turbine blade is defined in terms of the location and quantity of ice mass using experimental modal analyses. An artificial neural network model is trained with a data set (natural frequencies and ice masses) generated using that explicit relation. After training, this artificial neural network model is given an input of natural frequencies of the iced blade (identified from experimental modal analysis) corresponding to 18 test cases, and it identified ice masses’ location and quantity with a weighted average percentage error value of 17.53%. The proposed technique is also demonstrated on the NREL 5-MW wind turbine blade data.

Wind turbines installed in cold-climate regions are prone to the risks of ice accumulation which affects their aeroelastic behavior. The studies carried out on this topic so far considered icing in a few sections of the blade, mostly located in the outer part of the blade, and their influence on the loads and power production of the turbine are only analyzed. The knowledge about the influence of icing in different locations of the blade and asymmetrical icing of the blades on loads, power, and vibration behavior of the turbine is still not matured. To improve this knowledge, multiple simulation cases are needed to run with different ice accumulations on the blade considering structural and aerodynamic property changes due to ice. Such simulations can be easily run by automating the ice shape creation on aerofoil sections and two-dimensional (2-D) Computational Fluid Dynamics (CFD) analysis of those sections. The current work proposes such methodology and it is illustrated on the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine model. The influence of symmetrical icing in different locations of the blade and asymmetrical icing of the blade assembly is analyzed on the turbine’s dynamic behavior using the aeroelastic computer-aided engineering tool FAST. The outer third of the blade produces about 50% of the turbine’s total power and severe icing in this part of the blade reduces power output and aeroelastic damping of the blade’s flapwise vibration modes. The increase in blade mass due to ice reduces its natural frequencies which can be extracted from the vibration responses of the turbine operating under turbulent wind conditions. Symmetrical icing of the blades reduces loads acting on the turbine components, whereas asymmetrical icing of the blades induces loads and vibrations in the tower, hub, and nacelle assembly at a frequency synchronous to rotational speed of the turbine.