Abstract
Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.
Highlights
The combination of the ever-increasing global electricity demand and growing carbon emissions has in recent decades firmly positioned renewable energy generation as a key for securing the future energy provision for our needs
To address the limitations of existing reviews, this paper presents a systematic review of recent developments in this area and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines (WTs) and farms
Fiber Bragg grating (FBG) sensor measurement for WTs has increasingly been researched as a promising alternative condition monitoring (CM) technique due to its advantages such as lower signal-to-noise ratio, immunity to electromagnetic interference, small sensor size, flexibility, multiplexing, and multi-physical sensing capability [28,71,72,73]
Summary
The combination of the ever-increasing global electricity demand and growing carbon emissions has in recent decades firmly positioned renewable energy generation as a key for securing the future energy provision for our needs. Onshore and offshore wind turbines (WTs) often operate in harsh environments [3] This invariably imposes a requirement for sophisticated and powerful real-time condition monitoring (CM) systems that are capable of adapting to any environmental or operational condition during the conversion of kinetic energy into electricity. Energies 2021, 14, 5967 based techniques as well as data-driven and hybrid modeling procedures have been applied in this task [4]. The emergence of sensing technologies makes it easier to collect the relevant operating history, directing health CM research to go further toin this understanding task [4]. A attractive methodology that signals, in an effort to enable more reliable diagnosis and prognosis of subassembly holds great potential to enable advances in this area is machine learning (ML), especially failures and lifetime consumption.
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