1. Robust data filtering in wind power systems
By: Andrés Llombart-Estopiñán
CIRCE Foundation –
Zaragoza University

2. Index Objective Introduction: the need of filtering
The LMS fitting technique
The LMedS methodology
Experimental results
Conclusions

3. Index  Objective Introduction: the need of filtering
The LMS fitting technique
The LMedS methodology
Experimental results
Conclusions

4. Objective
To assess the performance of the Least Median of Squares method when it is used to filter wind power data

5. Index Objective  Introduction: the need of filtering
The LMS fitting technique
The LMedS methodology
Experimental results
Conclusions

6. Introduction Why it is needed? Operation Maintenance
Production Control
Characterization of the P – v curves
High quality P – v data

7. Introduction Circumstances that affect the data quality
Sensor accuracy
EMI
Information processing errors
Storage faults
Faults in the communication systems
Alarms in the wind turbine
etc

8. Introduction
An example of P – v data

9. Introduction
P – v data after considering the SCADA alarms

10. Index Objective Introduction: the need of filtering
 The LMS fitting technique
The LMedS methodology
Experimental results
Conclusions

11. The LMS fitting technique
Gets the curve that minimizes the Mean Square Error
All measurements can be interpreted with the same model
Very sensitive to outliers
Breakdown of 0% of spurious data

12. The LMS fitting technique

13. Index Objective Introduction: the need of filtering
The LMS fitting technique
 The LMedS methodology
Experimental results
Conclusions

14. The LMedS fitting technique
It is based in the existence of redundancy
LMedS method uses the Median whereas the LMS method uses the mean
Unfortunately the LMedS method don’t have analytical solution

15. The LMS fitting technique1 2 3 4 5 6 8 7

16. The LMedS fitting technique
Example
Fitting with a polynomial with 4 coefficients
n measurements
m possible solutions, where

17. The LMedS fitting technique
Steps to get the fitting:
Calculate the m subsets of the minimum number of measurements required to fit your curve
For each subset S, we compute a power curve in closed form PS
For each solution PS, the median MS of the squares of the residue with respect to all the measurements is computed
We store the solution PS which gives the least median MS

18. The LMedS fitting technique
Rejection of wrong data:
Estimate de standard deviation
Probability of accepting a measure being good: 99 %
Threshold = 2.57 s

19. Index Objective Introduction: the need of filtering
The LMS fitting technique
The LMedS methodology
 Experimental results
Conclusions

20. Experimental results Methodology A year of historical data
5 different tests
Alarm Records (AR)
AR + classical statistic method
AR + robust statistic
Classical statistic
Robust statistic

21. Experimental results
Rough data
Considered Alarms

22. Experimental results
AR + Class. Stat
AR + Robust Stat.

23. Experimental results
Classic Stat.
Robust Stat.

24. Index Objective Introduction: the need of filtering
The LMS fitting technique
The LMedS methodology
Experimental results
 Conclusions

25. Conclusions A robust filtering method has been proposed
It has been proved successfully
The method have shown a good robustness
Some research is needed
Considering the wind direction

26. Robust data filtering in wind power systems
Thanks for your attention

27. The LMedS fitting technique
Example: fitting a polynomial of 4 coefficients for a 3 months period of data, that implies ~ data
The computational cost is huge

28. The LMedS fitting technique
Solution: selecting randomly subsets
Compromise:
Minimizing the number of subsets
Warranting a reasonable probability of not failing
So, the first method step is substituted by a Monte Carlo technique to randomly select k subsets of 4 elements

29. The LMedS fitting technique
How many subsets?
A selection of k subsets is good if at least in one subset all the measurements are good
Pns is the probability that a measurement is not spurious
Pm is the probability of not reaching a good solution

30. The LMedS fitting technique
In our example considering:
Pns = 75 %
Pm = 0,001