MATLAB and Simulink Seminars

Application of Artificial Neural Networks for Condition Monitoring of Wind Turbine Main Bearings

Event Type Start Time End Time
Webex 18 Nov 2021 - 1:00 PM CST 18 Nov 2021 - 2:00 PM CST

Overview

In a wind turbine, a failure of an important component, such as a main bearing, can lead to long-lasting downtimes and thus to a corresponding energy loss. In offshore wind energy, the problem is even more serious as maintenance work is not always possible due to adverse weather conditions and must be planned in advance. In order to save operational expenditure, wind farm operators are required to implement a maintenance strategy that enables them to predict a component’s failure as early as possible.

The RWE Renewables GmbH has developed an ANN based tool that predicts the temperature of undamaged main bearings based on a selection of SCADA signals. Anomalies are detected when the actual bearing temperature deviates from the predicted temperature. The tool was shown to be successful in detecting issues up to nine months before failure.

About the Presenter

Niels Jessen, Wind Turbine Performance Analyst
RWE, Germany

Niels studied mechanical engineering with focus on sustainable energy at Hamburg University of Applied Sciences (HAW Hamburg). Wrote his master thesis about the application of artificial neural networks for condition monitoring purposes of offshore wind turbines. Works since 2019 as Wind Turbine Performance Analyst at RWE Renewables.

condition-monitoring-wind-turbine-main-bearings

To continue, please disable browser ad blocking for mathworks.com and reload this page.

You are already signed in to your MathWorks Account. Please press the "Submit" button to complete the process.