# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
###### Abstract  
In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system's failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time. wind turbine; fault diagnosis; renewable energy; Extreme Learning Machines (ELM); feature engineering; normal behaviour models Article  
Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines April 202111990  
ing failures in the wind turbines using SCADA data [6]. Usually, the approaches for condition-monitoring based on SCADA data are grouped into Signal Trending techniques, Artificial Neural Networks (ANN) and Physical Models. Traditionally, when SCADA data is used to forecast the health status of WTs, classification models are used to predict failure states. Two critical problems can make classification models not reliable: (1) On the one hand, the classes are extremely unbalanced with an over-representation of the normality state and a very small representation of the failure state (warning/alarm), which is specific to each subsystem. (2) On the other hand, the labelling of the data needed to train the models is not always reliable, as explained before, because it is done automatically and contain many errors. This is why in this paper, the use of normality models is proposed for predicting one or more variables generated by a subsystem, using the other variables in the subsystem and detecting that the prediction is out of the norm when the subsystem deteriorates. A very efficient computational way to develop normality wind turbine subsystem models is presented. It is based on Single-hidden Layer Feedforward Networks (SLFNs), a type of single-layer artificial neural network, which are trained using the Extreme Learning Machine (ELM) technique in order to optimise the developing process. This strategy has been implemented using ELM and results have been compared using other well-known machine learning approaches such as PLS, SVM or DANN. Two main experiments are carried out to demonstrate the utility of the normality models. The first one (model of the generator subsystem) is used to exemplify how to build a SLFN normality model trained using the ELM strategy. It is a simple model used to highlight how the dimension of the network is fundamental in order to avoid over-training and is also used to explore the two different approaches for deriving the model: deriving one for each individual turbine of the plant, or grouping all the turbines together to derive a global model for the whole plant. This study makes it possible to check the differences between the models obtained from these two different approaches, for the same subsystem.