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1. Normal Behavior Models for Wind Turbine
Vibrations
Comparison of Neural Networks
and a Stochastic Approach
Organized By: Fatima Koubba
Presented to: Dr. Mohamad Naja

2. Table Of Contents:
• Introduction and Objective
• Methods :
1. Neural Networks: A Deterministic Approach
2. Stochastic Approach: The Langevin Model
• Comparing Both Approaches
• Conclusions

3. Introduction and Objective:
Wind turbines are devices that produce electricity using wind power.
Vibrations can cause damage to the turbine and reduce its efficiency.
1. Blade vibrations
2. Tower vibrations
 Models are created to predict how the tower top accelerates vibrates normally

4. First method : Neural Network Approach
It have been proven to perform better than models based on regression analysis
Neural networks transform input variables with a linear coefficient and a non-linear function
 Using three previous wind speeds and tower top
accelerations collected in October 2014
 Use wind speeds and the previous acceleration value
collected in November 2014
 Useful method for predicting the tower top acceleration
signal

5. Second method: Stochastic Approach
Using statistical methods to model the random behavior of the system
The approach assumes that the random behavior of the wind turbine can be described by a
probability distribution
 Instead of computing non-linear functions and
weights , retrieves two single functions of the
variables involved
 D(1) the deterministic contribution
 D(2) the stochastic fluctuations

6. The mean, which represents the average value of the dataset
The variance,
which
represents the
spread of the
dataset
The skewness, which measures the degree of asymmetry in the distribution
The kurtosis,
which measures
the degree of
flatness in the
distribution

7. One concludes that the stochastic approach results have comparable
accuracy or are even more accurate than the results from NN
models

9. Conclusion
 Both models provide a good estimate for the central part of the original signal
 A normal behaviour model based on the stochastic approach provides more information
 When using low-frequency data, the NN is as good
 NNs are a good choice to predict average values or less fluctuating signals
 The deterministic part of the Langevin equation used in the stochastic approach can also
provide