Abstract
Wind energy is one of the most relevant renewable energy. The maintenance of wind turbines is essential to ensure reduced operation and maintenance costs. Supervisory control and data acquisition system acquires large volumes of data from different condition monitoring systems. Artificial intelligence algorithms are employed to obtain reliable information, although redundant information is usually employed decreasing the validity of the results. It is proposed an approach based on data reduction employing data filtering, correlation, principal component analysis to reduce redundant information introduced in neural networks. A real case study is proposed with data from real win turbine is used to develop a case study analyzing one critical alarm. The results obtained prove the validity of the methodology, reducing the initial dataset and increasing the reliability of the neural network.
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Acknowledgements
The work reported herewith has been financially by the Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102)
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Segovia Ramírez, I., Bernalte Sánchez, P.J., García Márquez, F.P. (2022). A Supervisory Control and Data Acquisition System Filtering Approach for Alarm Management with Deep Learning. In: García Márquez, F.P. (eds) International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. IEMAICLOUD 2021. Smart Innovation, Systems and Technologies, vol 273. Springer, Cham. https://doi.org/10.1007/978-3-030-92905-3_10
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DOI: https://doi.org/10.1007/978-3-030-92905-3_10
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