Abstract
It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines.
Highlights
Each year, the wind sector has profit losses due to wind turbine failures that can range from around 200 M€ in Spain or 700 M€ in Europe to 2200 M€ in the rest of the world
All results are obtained with k = 1 and we can see that the F1-score is close to 1 and highly correlated with the Classification Rate (CR) results
Experimental results using the 36 sensor variables listed in Table 2 show that conditional mutual information (CMI) algorithm obtains good CR for all the wind turbine with up to six features and only one neighbour
Summary
The wind sector has profit losses due to wind turbine failures that can range from around 200 M€ in Spain or 700 M€ in Europe to 2200 M€ in the rest of the world. We refer the reader to [30] where a detailed explanation of these techniques can be found Authors such as [31] have used a different type of ANN, Neuro-Fuzzy Inference System (ANIFS), to characterize normal behaviour models in order to detect abnormal behaviour of the captured signals using the prediction error to indicate component malfunctions or faults; whil [32] use an ANN to perform a regression using two to four input variables and one output variable. An important contribution of this work is that it carries out the tasks of cleaning and sampling, which are necessary when dealing with real data, the selection of variables is done manually Works such as [36] use an ensemble of models based on ANN, Boosting Tree Algorithm (BTA), Support Vector Machine (SVM), Random Forest (RF) or Standard Classification and Regression Tree (CART), generating an interval of probability of failure.
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