Performance monitoring of wind turbines : a data-mining approach

• Main Author: Verma, Anoop Prakash
• Other Authors: Kusiak, Andrew
• Format: Others
• Language: English
• Published: University of Iowa 2012
• Subjects: Classification; Data mining; Fault prediction; Outlier detection; regression; Wind turbine; Industrial Engineering
• Online Access: https://ir.uiowa.edu/etd/3398; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=3343&context=etd

The rapid growth of wind turbines in terms of turbine size, number of installations and rated capacity has a huge impact on its operations and maintenance costs. Monitoring the performance of wind turbines and early fault prediction is highly desirable. To date, traditional maintenance strategies such as reactive maintenance, periodic maintenance etc. are more prevalent in wind industry. However, over the last couple of years, the research pertaining to wind turbine has been shifted towards the condition monitoring and maintenance. Condition monitoring approaches have shown their potential in wind industry by providing continuous monitoring of the wind turbines, and identifying fault signatures in the event of faults. However, most of the studies reported in literature are based on the simulated dataset, or in constrained experiments. In reality, the external environment plays an important role in governing the turbine operations. Moreover, the cost associated with condition monitoring cannot be justified as it often requires installations of specific sensors, equipment. Another stream of research focuses on utilizing historical turbine data for turbine performance assessment in real time. The cost associated with such approaches is almost negligible as most of the wind farms are equipped with SCADA systems which records turbine performance data in regular time-interval. Such approaches are called as performance monitoring. In this dissertation, the performance monitoring of wind turbines is accomplished using the historical wind turbine data. The information from SCADA operational data, and fault logs is used to construct accurate models predicting the critical wind turbine faults. Depending upon the nature of turbine faults, monitoring wind turbines with different objectives is studied to accomplish different research goals. Two research directions of wind turbines performance are pursued, (1) identification and prediction of critical turbine faults, and (2) monitoring the performance of overall wind farm. The goal of predicting critical faults is to facilitate planned maintenance, whereas, monitoring the performance of overall wind farm provides the status-quo of all wind turbines installed in a wind farm. Depending on the requirement, the performance of overall wind farm can be assessed on a daily, weekly, or monthly basis. Solution methodologies presented in the dissertation are generic enough to be applicable to other industries such as wastewater treatment facilities, flood prediction, etc.