A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults

© 2017 IEEE. This paper presents a neural adaptive fault tolerant control design of wind turbines in partial load operation. The controller is designed to be robust against actuator faults as well as noise, while keeping the wind turbine generating as much power as possible. The wind speed variation...

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Main Authors: Habibi, H., Nohooji, H., Howard, Ian
Format: Conference Paper
Published: 2018
Online Access:http://hdl.handle.net/20.500.11937/68341
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author Habibi, H.
Nohooji, H.
Howard, Ian
author_facet Habibi, H.
Nohooji, H.
Howard, Ian
author_sort Habibi, H.
building Curtin Institutional Repository
collection Online Access
description © 2017 IEEE. This paper presents a neural adaptive fault tolerant control design of wind turbines in partial load operation. The controller is designed to be robust against actuator faults as well as noise, while keeping the wind turbine generating as much power as possible. The wind speed variation is considered as an external disturbance, and an adaptive radial basis function neural network is utilized to estimate aerodynamic torque. Estimation of a fault size and establishment of a desired trajectory are adopted in the design. Using the proposed method, the reliability of wind power generation is increased so as to track the optimum power point under faulty conditions, close to the fault free case. Uniformly ultimately boundedness of the closed-loop system is achieved using Lyapunov synthesis. The designed controller is verified via numerical simulations, showing comparison with an industrial reference controller, using predefined criteria.
first_indexed 2025-11-14T10:40:59Z
format Conference Paper
id curtin-20.500.11937-68341
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T10:40:59Z
publishDate 2018
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-683412021-02-16T07:50:50Z A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults Habibi, H. Nohooji, H. Howard, Ian © 2017 IEEE. This paper presents a neural adaptive fault tolerant control design of wind turbines in partial load operation. The controller is designed to be robust against actuator faults as well as noise, while keeping the wind turbine generating as much power as possible. The wind speed variation is considered as an external disturbance, and an adaptive radial basis function neural network is utilized to estimate aerodynamic torque. Estimation of a fault size and establishment of a desired trajectory are adopted in the design. Using the proposed method, the reliability of wind power generation is increased so as to track the optimum power point under faulty conditions, close to the fault free case. Uniformly ultimately boundedness of the closed-loop system is achieved using Lyapunov synthesis. The designed controller is verified via numerical simulations, showing comparison with an industrial reference controller, using predefined criteria. 2018 Conference Paper http://hdl.handle.net/20.500.11937/68341 10.1109/ANZCC.2017.8298486 restricted
spellingShingle Habibi, H.
Nohooji, H.
Howard, Ian
A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults
title A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults
title_full A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults
title_fullStr A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults
title_full_unstemmed A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults
title_short A neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults
title_sort neuro-adaptive maximum power tracking control of variable speed wind turbines with actuator faults
url http://hdl.handle.net/20.500.11937/68341