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Sensor-less maximum power extraction control of a hydrostatic tidal turbine based on adaptive extreme learning machine

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Yin, Xiuxing and Zhao, Xiaowei (2020) Sensor-less maximum power extraction control of a hydrostatic tidal turbine based on adaptive extreme learning machine. IEEE Transactions on Sustainable Energy, 11 (1). pp. 426-435. doi:10.1109/TSTE.2019.2894064 ISSN 1949-3029.

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Official URL: http://dx.doi.org/10.1109/TSTE.2019.2894064

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Abstract

In this paper, a hydrostatic tidal turbine (HTT) is designed and modelled, which uses more reliable hydrostatic transmission to replace existing fixed ratio gearbox transmission. The HTT dynamic model is derived by integrating governing equations of all the components of the hydraulic machine. A nonlinear observer is proposed to predict the turbine torque and tidal speeds in real time based on extreme learning machine (ELM). A sensor-less double integral sliding mode controller is then designed for the HTT to achieve the maximum power extraction in the presence of large parametric uncertainties and nonlinearities. Simscape design experiments are conducted to verify the proposed design, model and control system, which show that the proposed control system can efficiently achieve the maximum power extraction and has much better performance than conventional control. Unlike the existing works on ELM, the weights and biases in the ELM are updated online continuously. Furthermore, the overall stability of the controlled HTT system including the ELM is proved and the selection criteria for ELM learning rates is derived. The proposed sensor-less control system has prominent advantages in robustness and accuracy, and is also easy to implement in practice.

Item Type: Journal Article
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Tidal power, Hydrostatics, Pumping machinery
Journal or Publication Title: IEEE Transactions on Sustainable Energy
Publisher: IEEE
ISSN: 1949-3029
Official Date: January 2020
Dates:
DateEvent
January 2020Published
18 January 2019Available
12 January 2019Accepted
Volume: 11
Number: 1
Page Range: pp. 426-435
DOI: 10.1109/TSTE.2019.2894064
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 3 April 2019
Date of first compliant Open Access: 4 April 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/R007470/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

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