Intelligent condition monitoring for wind turbines

EU-funded researchers have developed an advanced system for continuously monitoring wind turbines components, which promises to improve machinery reliability by up to 50 %.

Digital Economy

With the project CMSWIND (Advanced condition monitoring system for the assessment of wind turbines rotating parts), the researchers looked into the feasibility of combining three new techniques to monitor the condition of in-service wind turbines. Vibration, motor current signature and acoustic emission analysis were designed to monitor the condition of the gearbox, rotary components and generator, respectively. These components together are estimated to account for approximately 22 % of wind turbine downtime. For validation, the developed prototype was installed on a Vesta V90-3.0 MW within the Bandirma Wind Energy Power Plant in Turkey. The new monitoring system remained on the wind turbine for six months. Data gathered during this period were stored in bins according to power output and rotor speed. When the data processing had been completed, it was concluded that the health of the wind turbine machinery had not been affected while the CMSWIND system was operational. The operator had also not identified any malfunction or failure during the same period. A baseline representative of the wind turbine’s healthy status was then determined for the prototype. Parameters extracted from new measurements, such as the root mean square of all waveforms, are compared with the baseline. A warning is generated if parameters go beyond set limits. When a specific number of warnings is exceeded, an alert indicating potential damage to the wind turbine is then released. The CMSWIND system detects deviations from the healthy state and provides a probability that they represent a defect by using baselines. The incorporated alert system informs machine operators of potential damage to the wind turbine machinery as soon as it occurs. While developing the new condition monitoring system, researchers also investigated its potential for commercialisation. They carried out an exhaustive analysis of lifetime costs of wind energy assets, considering operation and maintenance costs as well as the impact of component failure on the farm power output. Allowing early detection of defects in wind turbine components, the CMSWIND system can help optimise the maintenance schedule. Unnecessary maintenance and out-of-service wind turbines can even be eliminated lowering the costs of electricity generated.