Feature Selection Algorithms for Wind Turbine Failure Prediction

Pere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó and Jordi Solé-Casals
Additional contact information
Pere Marti-Puig: Data and Signal Processing Group, University of Vic—Central University of Catalonia, c/ de la Laura 13, 08500 Vic, Catalonia, Spain
Alejandro Blanco-M: Data and Signal Processing Group, University of Vic—Central University of Catalonia, c/ de la Laura 13, 08500 Vic, Catalonia, Spain
Juan José Cárdenas: Smartive-ITESTIT SL, Carretera BV-1274, Km1, 08225 Terrassa, Catalonia, Spain
Jordi Cusidó: Smartive-ITESTIT SL, Carretera BV-1274, Km1, 08225 Terrassa, Catalonia, Spain
Jordi Solé-Casals: Data and Signal Processing Group, University of Vic—Central University of Catalonia, c/ de la Laura 13, 08500 Vic, Catalonia, Spain

Energies, 2019, vol. 12, issue 3, 1-18

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.

Keywords: feature selection; failure prediction; wind energy; health monitoring; sensing systems; wind farms; condition monitoring; SCADA data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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