Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors

• Main Authors: João Pacheco, Gustavo Oliveira, Filipe Magalhães, Carlos Moutinho, Álvaro Cunha
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Energies
• Subjects: onshore wind turbine; operational modal analysis; damage detection; monitoring layouts; sensors noise
• Online Access: https://www.mdpi.com/1996-1073/14/2/441

The reduction in operating and maintenance costs of wind farms is a fundamental element to guarantee the competitiveness and growth of the wind market. Wind turbines are highly dynamic structures prone to wear during their lifetime. Therefore, dynamic monitoring systems represent an excellent option to continuously evaluate their structural conditions. These systems allow early detection of damages, permit a proactive response, minimising downtime, and maximising productivity. In this context, the present paper describes the main results obtained with alternative instrumentation strategies tested in a 2.0 MW onshore wind turbine to reduce the costs of the monitoring equipment and at the same time ensure an adequate accuracy in structural condition evaluation. The data processing strategy encompasses the use of operational modal analysis combined with algorithms that deal with the particularities of operation of the wind turbines to continuously track the main vibration modes. After this automated online identification, the influence of the environmental and operating conditions on the tracked natural frequencies is mitigated, making the detection of abnormal variations of the natural frequencies possible, which might flag the appearance of damage. A database of continuously collected acceleration time series during one year is adopted to test the efficiency of alternative monitoring system layouts in detecting simulated damage scenarios. The tested alternative monitoring layouts present a varying number of sensors, alternative distributions in the wind turbine tower, and different sensor noise levels.