Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders

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.