Detection of blade icing and its influence on wind turbine vibrations

Wind turbine installations in extreme conditions like cold climate have increased over thelast few years and expected to grow in future in North America, Europe, and Asia regions due to good wind resources and land availability. Their installed capacity could reach 186 GW by the end of 2020. The cold climate sites impose the risk of ice accumulation on turbines during the winter due to the humidity at low temperatures. Since the atmospheric and operating conditions of the wind turbine leading to blade icing vary stochastically in space and time, the resulting ice accumulation is completely random, it is even different for turbines within the same site. Ice accumulation alters aerofoil shapes of the blade, affecting their aeroelastic behavior. The icing severity at different locations of the blade and their non-uniform distribution on blades have a distinct influence on turbine power output and vibrations. The current thesis proposes a methodology to investigate such behavior of wind turbines by considering the structural and aerodynamic property changes in the blade due to icing. An automated procedure is used to scale simulated/measured ice shape on aerofoil sections of the blade according to a specified ice mass distribution. The aeroelastic behavior of the blades is simulated considering the static aerodynamic coefficients of the iced aerofoil sections. The proposed methodology is demonstrated on the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine model. The method can be leveraged to analyze the influence of icing on any wind turbine model. De/Anti-icing systems are installed on the turbines to mitigate the risks associated with icing. It is essential to detect icing at the early stage and initiate these systems to avoid production losses and limit the risks associated with ice throw. Ice accumulation increases blade mass and its spatial distribution changes natural frequencies of the blade. A detection technique is proposed in this thesis to characterize ice mass distribution on the blades based on its natural frequencies. The detection technique is validated using experiments on a small-scale cantilever beam and 1-kW wind turbine blade set-ups and its effectiveness is also verified on large-scale wind turbine blades using numerical models. The proposed technique has the potential for detecting ice masses on large wind turbines operating in cold climate as it requires only first few natural frequencies of the blade. These natural frequencies are usually excited by the turbulent wind in operation/standstill conditions and they can be estimated from the vibration measurements of the blade