Abstract
Wind turbines (WTs) are often operated in harsh and remote environments, thus making them more prone to faults and costly repairs. Additionally, the recent surge in wind farm installations have resulted in a dramatic increase in wind turbine data. Intelligent condition monitoring and fault warning systems are crucial to improving the efficiency and operation of wind farms and reducing maintenance costs. Gearbox is the major component that leads to turbine downtime. Its failures are mainly caused by the gearbox bearings. Devising condition monitoring approaches for the gearbox bearings is an effective predictive maintenance measure that can reduce downtime and cut maintenance cost. In this paper, we propose a hybrid intelligent condition monitoring and fault warning system for wind turbine's gearbox. The proposed framework encompasses the following: a) clustering filter- (based on power, rotor speed, blade pitch angle, and wind speed signals)-using the automatic clustering model and ant bee colony optimization algorithm (ABC), b) prediction of gearbox bearing temperature and lubrication oil temperature signals- using variational mode decomposition (VMD), group method of data handling (GMDH) network, and multi-verse optimization (MVO) algorithm, and c) anomaly detection based on the Mahalanobis distances and wavelet transform denoising approach. The proposed condition monitoring system was evaluated using 10 min average SCADA datasets of two 2 MW on-shore wind turbines located in the south of Sweden. The results showed that this strategy can diagnose potential anomalies prior to failure and inhibit reporting alarms in healthy operations.
Highlights
Wind energy is currently widely used in several countries as a clean, cost-effective and sustainable source of renewable energy [1]
Detection analysis In this phase, the Mahalanobis distance is applied for assessing the deviations between the true values of the temperature signals and their forecasted values obtained from the hybrid forecasting model (VMD-Group Method of Data Handling (GMDH)-Multi-Verse Optimization (MVO))
EXPERIMENTAL RESULTS In this paper, the Supervisory Control And Data Acquisition (SCADA) data and maintenance information of two wind turbines have been used in order to evaluate the proposed model for anomaly condition monitoring of wind turbine’s gearbox
Summary
Wind energy is currently widely used in several countries as a clean, cost-effective and sustainable source of renewable energy [1]. Wind turbines’ operation in harsh environment and in the presence of highly variant stochastic loads, makes them prone to sensor, actuator and component faults, thereby requiring increased frequency of planned maintenance scheduling [2], [3].This latter, leads to higher maintenance costs and increased downtime and subsequently reduced power production. To lower the cost of maintenance, decrease downtime and improve wind turbine’s reliability, in the presence of faults, various condition monitoring techniques based on data obtained by the wind turbine’s Supervisory Control And Data Acquisition (SCADA) system have been proposed in the literature [4]–[7].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
