Indirect detection of ice on wind turbine blades can be achieved by monitoring changes in turbine behavior compared to historical baseline data. Five statistical process control methods were tested that calculate the distance between current and baseline measurements to detect icing. Detection accuracy was highest around 50-80% of rated wind speed and could reach 90% in simulations, but real data testing produced some false alarms. The methods provide a simple ice warning signal without additional sensors by analyzing multiple turbine variables simultaneously.

1. Indirect detection of ice on wind
turbine blades
Timo Karlsson, VTT
Operating Principle Detection accuracy
Wind turbine icing is a significant problem for wind power The main metrics used to compare different methods were
applications in cold climate conditions. Icing can cause fatigue detection accuracy and speed. In the simulated datasets
in turbine components, shortening the lifetime of the turbine and detection accuracy can be calculated as a percentage of
can cause immediate production losses. This work presents one correctly detected iced data points and speed is evaluated as
approach to ice detection indirectly, without additional sensors. number of steps between the start of an icing event and the first
Ice is detected by monitoring changes in turbine behaviour. issued alarm.
All the methods used here follow a similar two-step approach: The detection accuracy and speed depend on the severity of
First a baseline relationship between wind and different process icing and the wind speed during the icing incident. Best
variables is determined using historical data. Then real-time accuracy can be achieved at wind speeds at around 50 - 80 %
measurements are compared to the historical baseline. Possible of the rated wind speed.
icing events are identified by monitoring the differences
between the predetermined baseline and the measurements.
The drop in accuracy at low and high wind speeds happens
because the used process variables have smaller relative
Ice detection methods deviation from the reference dataset at these wind speeds. Also
at lower speeds the signal to noise ratio is high enough to have
The distance between current measurement and the reference an effect.
dataset is calculated using five different statistical process
control methods. The multivariate methods used make it
At best, when using simulated data at appropriate wind speed
possible to compare behaviour of multiple variables
these methods can reach 90% accuracy, but only with simulated
simultaneously and also take the correlations between different
data with very little noise.
variables into account.
The methods operate solely based on historical data. The same
Correctly detected icing events in the Time to first alarm in the medium wind
methods can be used on different types of turbines with very medium wind test case test case
minimal changes. 100 16
90
14
80
12
The result is a simple signal representing the state of the wind 70
S
10
turbine, higher signal values correspond with higher probability 60
MEWMA
a
m MEWMA
% 50 p 8
of icing. CUSUM
MCUSUM
l
CUSUM
MCUSUM
40 e 6
PCA s PCA
30 kNN kNN
4
Ice Warning signal in simulated test case, icing event starts at 60 stops at 120 20
2
1200 10
icing starts icing stops 0 0
Start of icing Light Icing Moderate
Start of icing Light Icing Moderate
1000 Icing Icing
Ice Case Ice cases
800
Detection statistics of the best case scenario from the simulation study. During these runs,
wind speed was between 8 and 11 m/s.
600
400 Testing with real data
200
Alarm Limit
The approach was tested on a real dataset collected from two
0
0 50 100 150 200
turbines in northern Finland over two years. The system can spot
likely icing events but introduces a number of false positive
Example of the behaviour of the ice warning signal during an icing incident in a simulated alarms in the process as well.
case. The beginning and the end of the ice incident are marked on the graph. An icing
alarm is issued for each timestep the value of the signal is above the alarm limit.
When comparing the warnings produced by the methods
introduced here to a commercial ice sensor, there does not seem
Simulation tests
to be a very strong correlation between the two. There are
simultaneous warnings but neither method was conclusive.
The methods were tested against a dataset produced using
FAST and the NREL reference 5 MW turbine model. The tests More work needs to be done to improve the detection accuracy
were run in several icing cases in different wind conditions in when dealing with real world data, but the preliminary result look
order to gauge the effectiveness of the approach. Performance useful.
was evaluated against different ice cases and different wind
scenarios for five different methods.