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Research Papers: Offshore Technology

Load Torque Estimation Method to Design Electric Drivetrains for Offshore Pipe Handling Equipment

[+] Author and Article Information
Witold Pawlus

Department of Engineering Sciences,
University of Agder,
P.O. Box 509,
Grimstad N-4898, Norway
e-mail: witold.p.pawlus@ieee.org

Martin Choux

Department of Engineering Sciences,
University of Agder,
P.O. Box 509,
Grimstad N-4898, Norway
e-mail: martin.choux@uia.no

Michael R. Hansen

Department of Engineering Sciences,
University of Agder,
P.O. Box 509,
Grimstad N-4898, Norway
e-mail: michael.r.hansen@uia.no

Geir Hovland

Department of Engineering Sciences,
University of Agder,
P.O. Box 509,
Grimstad N-4898, Norway
e-mail: geir.hovland@uia.no

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received August 5, 2015; final manuscript received February 9, 2016; published online April 7, 2016. Assoc. Editor: Charles E. Smith.

J. Offshore Mech. Arct. Eng 138(4), 041301 (Apr 07, 2016) (9 pages) Paper No: OMAE-15-1082; doi: 10.1115/1.4032897 History: Received August 05, 2015; Revised February 09, 2016

One of the main design objectives for electric drivetrains operating in offshore drilling equipment is to keep them as small, yet as effective, as possible, to minimize space they occupy on drill floor and maximize their performance. However, practical experience shows that typically choices made by design engineers are too conservative due to the lack of enough data characterizing load conditions. This results in too costly and too heavy selected components. Therefore, in the current paper we present a method to estimate required full-scale motor torque using a scaled down experimental setup and its computational model. A gripper arm of an offshore vertical pipe handling machine is selected as a case study for which the practical significance of the current work is demonstrated. The presented method has a potential to aid design of electrically actuated offshore drilling equipment and help design engineers choose correctly dimensioned drivetrain components.

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References

Figures

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Fig. 1

Induction motor: a dynamic inverse-Γ-equivalent circuit[14]

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Fig. 2

FOC of induction machine [14]

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Fig. 3

Procedure to establish and validate neural network to identify parameters of LuGre friction model

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Fig. 4

Structure of the neural network [14]

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Fig. 5

Vertical pipe handling machine (MH VPR)—courtesy of MHWirth

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Fig. 6

The gripper arm of MH VPR—courtesy of MHWirth

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Fig. 7

A simplified gripper arm with the winch drivetrain [18]

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Fig. 8

Test bench for running experiments

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Fig. 9

Control system and interface diagram for induction motors

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Fig. 10

Friction models establishment: (a) static friction model, (b) neural network performance, (c) LuGre friction—hysteresis, and (d) results benchmarking

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Fig. 13

Comparative analysis of winch motor operation—case III: mp = 37%⋅mswl and absolute speed amplitude n = 143%⋅nn

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Fig. 14

Comparative analysis of winch motor operation—case IV: mp = 0%⋅mswl and absolute speed amplitude n = 155%⋅nn

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Fig. 11

Comparative analysis of winch motor operation—case I: mp = 100%⋅mswl and absolute speed amplitude n = 39%⋅nn

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Fig. 12

Comparative analysis of winch motor operation—case II: mp = 100%⋅mswl and absolute speed amplitude n = 155%⋅nn

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