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Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders

descriptionPublicationkeyboard_double_arrow_right Article , Journal , Other literature type 03 Dec 2020 Spain Publisher:MDPI AGJournal:Applied Sciences, volume 10, page 8,649 (eissn: 2076-3417,

Authors: Mattia Beretta; Juan José Cárdenas; Cosmin Koch; Jordi Cusidó;

doi: 10.3390/app10238649

handle: 2117/336038

Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders

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Abstract

A hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to identify anomalous patterns within the input variables. Alarm logs are processed and merged to the anomaly detection output, creating a reliable health estimator of generator conditions. The proposed methodology has been tested on a fleet of 115 wind turbines from four different manufacturers located in various locations around Europe. The solution has been compared with other existing data modeling techniques offering impressive results on the fleet. An accuracy of 82% and a Kappa of 56% were obtained. The detailed methodology is presented using one of the available windfarms, composed of 13 onshore wind turbines rated 2 MW power. The rigorous evaluation of the results, the utilization of real data and the heterogeneity of the dataset prove the validity of the system and its applicability in an online operating scenario.

Country

Spain

Related Organizations

Keywords

SCADA data, Technology, Renewable energy, QH301-705.5, QC1-999, Alarms, Anomaly detection, Generator, predictive maintenance, wind turbines, Wind turbines, Biology (General), QD1-999, autoencoder, generator, T, Physics, Predictive maintenance, Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors, Autoencoder, Engineering (General). Civil engineering (General), renewable energy, anomaly detection, fault detection, Chemistry, alarms, Aerogeneradors -- Manteniment i reparació, TA1-2040, :Energies::Energia eòlica::Aerogeneradors [Àrees temàtiques de la UPC], Wind turbines--Maintenance and repair, Fault detection

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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%