Machine Learning Control for Floating Offshore Wind Turbine Individual Blade Pitch Control

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Kane:2020:ACC,

• author = "Michael B. Kane",
• booktitle = "2020 American Control Conference (ACC)",
• title = "Machine Learning Control for Floating Offshore Wind Turbine Individual Blade Pitch Control",
• year = "2020",
• pages = "237--241",
• abstract = "The cost of energy from current floating offshore wind turbines (FOWTs) are not economical due to inefficiencies and maintenance costs, leaving significant renewable energy resources untapped. Co-designing lighter less expensive FOWTs with individual pitch control (IPC) of each blade could increase efficiencies, decreases costs, and make offshore wind economically viable. However, the nonlinear dynamics and breadth of nonstationary wind and wave loading present challenges to designing effective and robust IPC for each desired location and situation.This manuscript presents the development, design, and simulation of machine learning control (MLC) for IPC of FOWTs. MLC has been shown effective for many complex nonlinear fluid-structure interaction problems. This project investigates scaling up these component-level control problems to the system level control of the NREL 5MW OC3 FOWT. A massively parallel genetic program (GP) is developed using MATLAB Simulink and OpenFAST that efficiently evaluates new individuals and selectively tests fitness of each generation in the most challenging design load case. The proposed controller was compared to a baseline PID controller using a cost function that captured the value of annual energy production with maintenance costs correlated to ultimate loads and harmonic fatigue. The proposed controller achieved 67percent of the cost of the baseline PID controller, resulting in 4th place in the ARPA-E ATLAS Offshore competition for IPC of the OC3 FOWT for the given design load cases.",
• keywords = "genetic algorithms, genetic programming, Training, Wind, Costs, Blades, Training data, Machine learning, Production",
• DOI = "doi:10.23919/ACC45564.2020.9147912",
• ISSN = "2378-5861",
• month = jul,
• notes = "Also known as \cite{9147912}",

}

Genetic Programming entries for Michael B Kane

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