Development of a wireless, non-intrusive, MEMS-based pressure and acoustic measurement system for large-scale operating wind turbine blades

Self Cite

Predictive maintenance and structural health monitoring are challenging and promising research fields today. In particular, cost-effective and long-term monitoring of wind turbines has been proven to be one of the key elements to successfully increase their efficiency. Accurate numerical modeling and real-time control-in-the-loop play an increasingly prominent role in understanding and optimizing blade aerodynamic and acoustic performances. A non-intrusive and modular measurement system is a prerequisite for long-term measurement campaigns in existing and future wind turbines. Current methods of performing aerodynamic and acoustic field measurements are cumbersome and expensive, leading to a shortage of aerodynamic and acoustic datasets on operating wind turbines. This paper demonstrates the ability of the new Aerosense system to operate successfully in the field. Aerosense is a long-lasting battery-operated and flexible wireless sensor node that can directly measure aerodynamic and acoustic effects on wind turbine blades. It consists of an array of state-of-the-art Micro-Electro-Mechanical Systems (MEMS) sensors, including 40 barometers and 10 microphones, combined with an ultra low power system-on-chip with wireless transmission over Bluetooth 5.1. Experimental results demonstrate the possibility of continuously acquiring data for up to four months on a single lithium battery of 8.7 Ah, featuring an absolute accuracy of 10Pa and an audio bandwidth of 6kHz.

Section: Aerosense: Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aerosense: Long-Range Bluetooth Wireless Sensor Node for Aerodynamic Monitoring on Wind Turbine Blades

Polonelli

Deparday

Müller

et al. 2022

Self Cite

“…However, in "real-life", the dynamic C L is calculated as the integral of the aerodynamic pressure distribution (more specifically the pressure coefficient curve) over an aerofoil section. This aerodynamic pressure is measured via sensors such as the ones we are developing in the Aerosense project [3] 1 .…”

Section: Problem Setupmentioning

confidence: 99%

“…In this work we propose an approach to identify LEE of a wind turbine blade by dynamically tracking moving and emerging clusters [5,13] of lift coefficients C L time-series signals form multiple sections along the span of the blade, under uncertain inflow conditions. This work is motivated by current efforts on development of novel micro-electro-mechanical-systems based aerodynamic surface pressure and aero-acoustic measurement systems for Structural Health Monitoring (SHM) of wind turbine blades, as part of the Aerosense project [3].…”

Section: Introductionmentioning

confidence: 99%

Identifying evolving leading edge erosion by tracking clusters of lift coefficients

Abdallah

Duthé

Barber

et al. 2022

Self Cite

This work proposes an approach to identify Leading Edge Erosion (LEE) of a wind turbine blade by tracking evolving and emerging clusters of lift coefficients CL time-series signals under uncertain inflow conditions. Most diagnostic techniques today rely on direct visual inspection, image processing, and statistical analysis, e.g. data mining or regression on SCADA output signals. We claim that probabilistic multivariate spatio-temporal techniques could play an eminent role in the diagnostics of LEE specifically leveraging CL time-series signals form multiple sections along the span of the blade. The proposed method extracts clusters’ features based on Variational Bayesian Gaussian Mixture Models (VBGMM) and tracks their spatial and temporal changes, as well as interpret the evolution of the clusters through prior physics-based assumptions. The parameters of the VBGMM are the mean, the eigenvalues and eigenvectors of the covariance matrix, and the angle of orientation of the eigenvectors. We show that the distribution of the CL data may not show statistically separable clusters, however, the parameters of the VBGMM clusters fitted to the CL data, allows to discriminate moving clusters primarily due to varying inflow and operating conditions, versus emerging clusters primarily due to evolving severity of the blade LEE.

“…Recent developments in electronics, wireless communication, and micro-electro-mechanical systems (MEMS) sensors are making it possible to acquire data in a cost-effective and energy efficient way [8]. Therefore, a smart measurement system that is thin, easy to install without damaging the blade, low power, self-sustaining and wirelessly transmitting is being developed in the Aerosense project [9]. One of the main objectives of the Aerosense project is to develop a MEMS-based surface pressure and acoustic smart measurement system, which can easily be installed on any wind turbine.…”

Section: Introductionmentioning

confidence: 99%

An experimental system to acquire aeroacoustic properties on wind turbine blades

Deparday

Müller

Polonelli

et al. 2022

Wind turbine noise is a key issue preventing the successful exploitation of the full potential of wind energy throughout the world, especially in urban areas. To better assess and predict wind turbine noise, several aeroacoustic simulations and models have been developed over the past. Many semi-empirical models for noise emission and propagation rely on aeroacoustic properties at the blade level, including the pressure gradient, the spectrum of the pressure fluctuations, the convection velocity and the coherence lengths. Field measurements of these local quantities on operating wind turbines are valuable to improve the accuracy of the models. In the Aerosense project, a cost-effective smart measurement system is being developed that is thin, easy to install without damaging the blade, low power, self-sustaining and wirelessly transmitting. This measurement system uses MEMS sensors, which require some calibrations and corrections to obtain sufficiently accurate data. This paper describes the experimental system and its workflow, which has been developed within the Aerosense project to obtain sufficiently accurate measurements for semi-empirical noise emission and propagation models. The experimental system and its workflow are then validated in an anechoic wind tunnel on a NACA63-418 airfoil. The results show that this experimental system is able to acquire relevant aeroacoustic properties on operating wind turbines.