Abstract
The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.
Highlights
IntroductionIntroduction and motivationDue to huge market demand and technological advances, the global installed wind power capacity has been growing steadily for many years, and wind power has been playing an important role in the power supply of many countries.[1,2,3] Over time, wind turbines (WTs) will inevitably age, and decline in performance and efficiency, whereas their operation and maintenance (O&M) cost will increase.[4,5,6] In addition, as WTs continue to become larger and larger,[7,8] the hub, nacelle, and other important parts become higher and higher off the ground, bringing challenges to the O&M of wind power equipment and pushing up the maintenance costs of the turbines
The supervisory control and data acquisition (SCADA) system is installed in most wind turbines (WTs) in service, and as a result, a considerable amount of historical SCADA data is generated as more and more WTs serve for many years
The potential is there to use these data to improve the technology of WT, condition monitoring (CM), and operation and maintenance (O&M) strategies by mining the historical SCADA data for WTs
Summary
Introduction and motivationDue to huge market demand and technological advances, the global installed wind power capacity has been growing steadily for many years, and wind power has been playing an important role in the power supply of many countries.[1,2,3] Over time, wind turbines (WTs) will inevitably age, and decline in performance and efficiency, whereas their operation and maintenance (O&M) cost will increase.[4,5,6] In addition, as WTs continue to become larger and larger,[7,8] the hub, nacelle, and other important parts become higher and higher off the ground, bringing challenges to the O&M of wind power equipment and pushing up the maintenance costs of the turbines. Controlling the cost of energy (COE) of wind power and keeping it financially viable is the issue that the wind power industry must face in order to continue its healthy development.[9]. Monitoring changes in the condition of the WTs or particular assemblies, and formulating optimized O&M strategies based on the condition information is an effective way to improve the service performance of WTs and reduce the energy cost of wind power.[10] This is a current research hotspot in the wind power field. In Refs.[11,12,13,14] the authors provide a review of the development of WT condition monitoring (CM) technology. The rapid development of AI and machine learning technologies in recent years has greatly facilitated the advancement of WT condition monitoring technology and the mushrooming of new methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
