By Jan Schuurmans, 14-9-2018

The wind turbine controller of wind turbines is rapidly advancing and becoming more complicated. The first variable speed wind turbine control systems 'just' controlled generator speed and generator power. They just needed 1 PID loop (for speed control by pitch), a generator torque speed curve, and a fine pitch schedule. Tuning the control system meant that just a couple of parameters, say less than 5, had to be tuned.

However, modern wind turbine controllers can easily run 8 or more PID loops in the wind turbine controller, extended with notch filters, lead-lag filters and low pass filter, aimed not just at speed control, but also at fore-aft acceleration control (to reduce tower bottom loads), and blade loads control (to reduce nacelle loads and blade loads). Tuning all of these parameters (more than 200!) is not that simple anymore, because nowadays we are talking about tuning hundreds of control parameters. For instance, tuning the PIDs for the generator speed control loop in combination with the fore-aft loop, is hard: these PIDs should not just be judged on damping and speed of response, but also on their (huge!) effect on loads and pitch activity.  

Traditionally, we 'manually' tuned the control system once, for a particular wind turbine configuration and put a hell of an effort in reducing the loads as much as possible while maximising power output. After the first (wind turbine design) iteration, our customers just continued with those settings while adjusting the configuration of the wind turbine, and only adjusting control parameters if necessary. There is simply not enough time for tuning. The end result is a non-optimal wind turbine design!

To change this, we started to experiment with automatic tuning methods. The idea is relatively simple: try to automate the way a control engineer (should) work. First, we developed an algorithm that computes control parameters based on a specified bandwidth of the closed-loop (wb). Next, we search for that wb value that minimises a costs function that penalises (weighted) loads versus power output. In this latter optimization, we apply data generated from load simulations on an (aero-elastic) model. 

graphs of the effect of bandwidth on loads and power

As an example, look at the graphs above. The bandwidth was varied (crosses) and for each value we computed the increase/decrease in power and Design Equivalent Loads (DELS), relative to an initial tuning. The wind turbine designer can now quickly assess the effect of tuning and decide for load more load reduction with less power output or the other way around. 

The algorithms discussed above are not specific to any wind turbine controller software and we do not need to know the details of your control software, to make this work. If you are interested, please contact us

by J. Schuurmans

DotX has succesfully developed, implemented and tested the optimization of the fine-pitch angles, and yaw angle.  

The pitch angles of a wind turbine are initially set by a mechanic using a screwdriver, or a button to fix the angle at which the blade is supposed to be at when at 0 degrees (the fine pitch angle). The mechanic aligns the markers on the blade and in the nose-cone of the turbine to do so. With our automatic control procedure, the wind turbine computer first adjusts the relative pitch angles to minimise rotor unbalance. After that, the computer adjusts all blade angles in unison to find the optimal fine pitch angle that maximises power output of the turbine.  

A similar procedure has been developed for the yaw angle: the yaw angle is varied in cycles, each lasting a fixed amount of time, until the optised yaw angle has been found, such that power output is maximised. The graph below shows how the optimal yaw offset converges to its final value, within approximately 100 cycles (that correspond to 30 'effective' hours, i.e. hours where the turbine operates in the right conditions). In this graph, the wind vane had been calibrated first with the aid of a LIDAR, to allow us checking the optimsed yaw angle. The LIDAR based yaw misalignment, shown as a black line, confirms the result.

optimisation of the yaw angle


Tests show that oscillations can be reduced by more than 80%, while yield can be improved 3-6% per year. The optimization procedures uses standard on-board sensors only. After optimization, a PV curve difference of 'before' and 'after' is generated that cane be used to calculate the exact yield improvement. This procedure has been verified by a certification institute and has been applied to over 20 wind turbines in the field.