Bayesian neural network model for estimating fatigue loads on wind turbines
Creators: name: Omole, John Adetoyese
name: Schlipf, David
name: Venu, Anish
name: Ludde, Mike
Publisher: name: Zenodo
Year: 2021
Subjects: Bayesian Neural Network, Probabilistic Machine Learning, Fatigue Loads, Load Estimation, Blade Loads

description: <strong>Presentation at the Wind Energy Science Conference 2021 </strong> The operation of wind turbines usually focuses on generating as much energy as possible over the lifetime while keeping the structural loads of mechanical components within their design limits. The loads are mainly caused by continuous change in mechanical stress levels due to stochastic variability of the turbine wind inflow. Thus, the monitoring of fatigue loads is very crucial for the operation and for possible lifetime extensions. However, continuous load measurements on each wind turbine is very expensive.Recent studies have shown that fully connected layer neural networks can be valuable to extract useful insights from both measurement and simulated load data [1, 2]. The application of Artificial Neural Network (ANN) techniques on wind turbine historical data has proved to accurately estimate loads on the turbine structural components based on Supervisory Control and Data Acquisition (SCADA) data only or with additional acceleration sensor data. Load measurements are only needed for the training of the neural network.The artificial neural network training optimizes the network parameters, i.e. bias and weight using maximum likelihood estimation method [3]. The ANN uses point estimation which is easy to compute but is prone to overfitting and overconfidence [4]. However, Bayesian Neural Network (BNN), introduced a probabilistic approach through the Bayesian Theorem in which the posterior distribution is modeled. The BNN uses probability distributions over possible values for the weights, rather than having a single value as the ANN, see Figure 1. Intuitively, the Bayesian neural network can then produce probabilistic guarantees on it’s estimations and also generate the distribution of the parameters that it has learnt from the observations and fine-tune its learning from the data to action decision making [5].In this project, we consider an onshore wind farm, containing eight 2 MW wind turbines from which one is equipped with load and acceleration measurements. The fatigue loads used in this project are measured in accordance with IEC TS 61400-13. The input data for the BNN are the SCADA and acceleration data, the output considered in this work are the blade flapwise bending moments.The raw data from the wind turbine undergoes several stages. First, the data is preprocessing which comprises the load calculation (Wöhler exponent of 12 for blades), data cleaning (removal of start-up and shut-down). Finally, a Principal Component Analysis is performed to avoid the curse of dimensionality and other problems mostly encountered in machine learning during training and prediction. The model initialization of the Bayesian neural network is done using a Gaussian distribution of the parameters. Then the probability distribution on the weights of the model is learned to give the predictive distribution of the outputs used in estimating the validation data.Here we used 50% of the data for training and 50% for validation. The BNN is able to provide a good estimate of the blade loads, see Figure 2. Overall, a coefficient of determination of 95.3% is reached. The deviation of the estimated loads from the measured ones as well as the uncertainties increases with the wind speed due to the fewer data points.In future work we will compare the results of the BNN to the ones from an ANN and quantify the uncertainty associated to the overall estimation by aggregating the model uncertainty with the measurement load uncertainty. <strong>Figures</strong> Link: Figure 1<br> Description: Scalar weights used for Artificial Neural Network (top) and probability distributions used as weights of<br> the Bayesian Neural Networks (bottom), based on [5].<br> Link: Figure 2<br> Description: Blade flap-wise damage equivalent loads estimated from SCADA and acceleration data: Mean<br> measured (red circle), mean estimated (blue dots), and uncertainties (black lines, +/-2 standard deviation of error between<br> mean estimated and mean measured, providing a 95% confidence) over 0.5 m/s wind bins. <strong>References</strong>:<br> [1] Nikolay Dimitrov, and Anand Natarajan 2019. From SCADA to lifetime assessment and performance optimization: how to<br> use models and machine learning to extract useful insights from limited data. Journal of Physics: Conference Series, 1222,<br> p.012032.<br> [2] Anish Venu, Mike Lüdde, and John Omole 2020. Prediction and Validation of Fatigue loads using Artificial Intelligence on<br> Real World Measurement Data. Journal of Physics: Conference Series, 1618, p.022006.<br> [3] C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.<br> [4] C. Blundell, J. Cornebise, K. Kavukcuoglu, and D.Wierstra, “Weight uncertainty in neural networks,” 2015.<br> [5] Laurent Valentin Jospin, Wray Buntine, Farid Boussaid, Hamid Laga, and Mohammed Bennamoun. (2020). Hands-on<br> Bayesian Neural Networks – a Tutorial for Deep Learning Users.