Optimisation of blades on large wind turbines with individual pitch control and trailing edge flaps

Zhenrong Jeremy Chen, PhD candidate, Dr Karl Stol, Senior Lecturer, Prof Brian Mace, Head of Department, Department of Mechanical Engineering

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Optimisation of blades on large wind turbines with individual pitch control and trailing edge flaps

Overview

Advanced load control methods for wind turbines such as individual blade pitch (whereby each blade on a wind turbine is pitched at a different angle) and trailing edge flaps (near the blade tips) have been shown in previous research to be capable of reducing fatigue loads on the turbine, with load reductions at the blade root being the most significant. This project aims to explore whether wind turbine blade designs are able to take advantage of the benefits offered by these load control methods to reduce the overall cost of energy ($/kWh) from the turbine over its lifespan.

Figure 1 Overview of optimisation process.

A process implemented in MATLAB which integrates blade design, structural analysis, turbine modelling, controller development and turbine simulation (time and frequency domain) is used with the SQP (Sequential Quadratic Programming) and NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) optimisation algorithms to find a particular blade geometry and layup which minimises the cost of energy, which accounts for the capital and operational costs of the turbine, and its energy production, over its lifetime. A list of the tools used in this process, illustrated in Figure 1, is included in Table 1.

Use of the University’s research virtual machines

The optimisation process developed makes use of virtual machines at several stages to parallelise task execution. Within the SQP optimisation, sensitivity analysis of each of the blade geometry design parameters is performed in parallel. Time domain simulations of wind turbines for system identification experiments at different wind speeds are also performed in parallel, along with additional simulations to determine the loads experienced by the turbine over its lifetime in a variety of wind fields and operational states as specified by international standards. From the results of these extensive simulations (typically around 80 separate simulations), constraints for the optimisation algorithms which correspond to limits on ultimate loads, fatigue loads and blade deflection are also calculated in parallel. The use of the computing resources from the NeSI Pan cluster greatly reduces the time required for each of these tasks, reducing time required for development of the optimisation process and allowing for results to be obtained within a realistic timeframe.

Figure 2 Sample results from NOMAD optimisation of blade layup.

Current Progress

The blade design and optimisation process is currently being used to optimise the blades on a baseline 5MW turbine with a standard collective pitch controller (no advanced load control). The result of this optimisation will be used as a starting point for optimisation of blades on a turbine with individual pitch control, with the use of localised actuators in the form of trailing edge flaps introduced at a later stage. Future work may also involve the optimisation of larger turbines (10-20MW) to assess the scale of benefits provided to large wind turbines which integrate advanced load control. Initial results have shown that there is room for the cost of energy of the baseline turbine to be reduced, and we aim to demonstrate how the benefits of advanced load control can extend past load reductions on existing blade designs.