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Title: Wave forecast and its application to the optimal control of offshore floating wind turbine for load mitigation

Journal Article · · Renewable Energy
 [1];  [1];  [2];  [3]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Glosten Associates, Seattle, WA (United States)
  3. GE Renewable Energy, Richmond, VA (United States)
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Control algorithms play an important role in energy capture and load mitigation for offshore floating wind turbines (OFWTs). One of the advanced and effective control techniques is the feedforward or model predictive control approach, which requires the forecast of incoming environment conditions. For OFWTs, wave loading is one of the dominant sources to excite structural responses. This study is thus motivated to develop forecasting algorithms for wave elevations and wave excitation forces with the purpose of applying feedforward controllers on OFWTs. Two forecasting algorithms, the approximate Prony Method based on ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) and SVM (Support Vector Machine) regression, are developed and validated using wave records from tank tests. Utilizing the forecasted wave elevations and wave excitation forces, a feedforward LQR controller is designed to mitigate structural loads of an OFWT system.

Research Organization:
Alstom Renewable US LLC, Greenwood Village, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
EE0005494; -EE0005494
OSTI ID:
1613299
Alternate ID(s):
OSTI ID: 1582754
Journal Information:
Renewable Energy, Vol. 128, Issue A; ISSN 0960-1481
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 44 works
Citation information provided by
Web of Science

References (6)

Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters journal July 2010
Parameter estimation for exponential sums by approximate Prony method journal May 2010
Fine tuning support vector machines for short-term wind speed forecasting journal April 2011
Short term wind speed prediction based on evolutionary support vector regression algorithms journal April 2011
Least Squares Support Vector Machine Classifiers journal June 1999
Evaluation of simple performance measures for tuning SVM hyperparameters journal April 2003

Cited By (1)

A Feedback Control Loop Optimisation Methodology for Floating Offshore Wind Turbines journal September 2019

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Related Subjects

17 WIND ENERGY
offshore floating wind turbine
wave forecast
optimal control
load reduction