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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
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WRAP-sensor-less-maximum-power-extraction-control-hydrostatic-turbine-machine-Zhao-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1415Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TSTE.2019.2894064
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 | ||||||||
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| Subjects: | T Technology > TC Hydraulic engineering. Ocean engineering T Technology > TJ Mechanical engineering and machinery |
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| Divisions: | Faculty of Science > 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: |
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| Volume: | 11 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 426-435 | ||||||||
| DOI: | 10.1109/TSTE.2019.2894064 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Publisher Statement: | © 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 | ||||||||
| RIOXX Funder/Project Grant: |
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