Application of FMRAC to fault‐tolerant cooperative control of a wind farm with decreased power generation due to blade erosion/debris buildup. (7th November 2017)

• Record Type: Journal Article
• Authors: Badihi, Hamed; Zhang, Youmin; Pillay, Pragasen; Rakheja, Subhash
• Other Names: Puig Vicenç guestEditor.; Odgaard Peter Fogh guestEditor.; Schulte Horst guestEditor.
• Is Part Of: International journal of adaptive control and signal processing. Volume 32:Number 4(2018)
• Page Start: 628
• Page End: 645
• Publication Date: 2017-11-07
• Subjects: fault detection and diagnosis (FDD) -- fault‐tolerant control (FTC) -- fault‐tolerant cooperative control (FTCC) -- fuzzy model reference adaptive control (FMRAC) -- wind farm -- wind turbines; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.2836
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: Wind energy has shown a remarkable potential for fulfilling the increasing world's energy demand in a clean and sustainable way. The wind energy industry has installed increasingly sophisticated and larger wind turbines particularly in offshore regions to capture such energy as efficiently and cost effectively as possible. The rapid growth in size and capacity of wind turbines together with harsh climate conditions and limited accessibility in offshore regions all result in higher failure rates and increased maintenance requirements and costs. Such difficulties motivate the use of advanced fault detection and diagnosis and fault‐tolerant control schemes in wind farms to improve their reliability and availability. Given the importance of this issue, this paper uses a fuzzy model reference adaptive control approach in a cooperative framework that is oriented to the design and development of a novel fault‐tolerant cooperative control scheme in a wind farm. This scheme handles decreased power generation faults in a wind farm caused by turbine blade erosion and debris buildup on the blades over time. The effectiveness and performance of the proposed scheme is demonstrated by a series of simulations on an advanced large offshore wind farm benchmark model in the presence of wind turbulence, measurement noise, load variations, and realistic fault scenarios.