0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Roos, F. , Johansson, H. , and Wikander, J. , 2006, “ Optimal Selection of Motor and Gearhead in Mechatronic Applications,” Mechatronics, 16(1), pp. 63–72. [CrossRef]
Pettersson, M. , and Ölvander, J. , 2009, “ Drive Train Optimization for Industrial Robots,” IEEE Trans. Rob., 25(6), pp. 1419–1424. [CrossRef]
Birk, L. , 2009, “ Application of Constrained Multi-Objective Optimization to the Design of Offshore Structure Hulls,” ASME J. Offshore Mech. Arct. Eng., 131(1), p. 011301. [CrossRef]
Tanaka, R. L. , and Martins, C. D. A. , 2011, “ Parallel Dynamic Optimization of Steel Risers,” ASME J. Offshore Mech. Arct. Eng., 133(1), p. 011302. [CrossRef]
Martins, M. R. , and Burgos, D. F. S. , 2011, “ Multi-Objective Optimization Design of Tanker Ships Via a Genetic Algorithm,” ASME J. Offshore Mech. Arct. Eng., 133(4), p. 041303. [CrossRef]
Ni, S. , Qiu, W. , Zhang, A. , and Prior, D. , 2015, “ Hydrodynamic Simulation and Optimization of an Oil Skimmer,” ASME J. Offshore Mech. Arct. Eng., 137(2), p. 021301. [CrossRef]
Siddiqi, A. , Bounova, G. , de Weck, O. L. , Keller, R. , and Robinson, B. , 2011, “ A Posteriori Design Change Analysis for Complex Engineering Projects,” ASME J. Mech. Des., 133(10), p. 101005. [CrossRef]
Arendt, J. L. , McAdams, D. A. , and Malak, R. J. , 2012, “ Uncertain Technology Evolution and Decision Making in Design,” ASME J. Mech. Des., 134(10), p. 100904. [CrossRef]
Camburn, B. , Dunlap, B. , Gurjar, T. , Hamon, C. , Green, M. , Jensen, D. , Crawford, R. , Otto, K. , and Wood, K. , 2015, “ A Systematic Method for Design Prototyping,” ASME J. Mech. Des., 137(8), p. 081102. [CrossRef]
Correa, P. , Pacas, M. , and Rodríguez, J. , 2007, “ Predictive Torque Control for Inverter-Fed Induction Machines,” IEEE Trans. Ind. Electron., 54(2), pp. 1073–1079. [CrossRef]
Yang, J. , and Zhu, G. , 2015, “ Adaptive Recursive Prediction of the Desired Torque for a Hybrid Powertrain,” IEEE Trans. Veh. Technol. 64(8), pp. 3402–3413. [CrossRef]
Papafotiou, G. , Kley, J. , Papadopoulos, K. , Bohren, P. , and Morari, M. , 2009, “ Model Predictive Direct Torque Control—Part II: Implementation and Experimental Evaluation,” IEEE Trans. Ind. Electron., 56(6), pp. 1906–1915. [CrossRef]
Harnefors, L. , 2003, Control of Variable-Speed Drives, Mälardalen University, Västerås, Sweden.
Pawlus, W. , Choux, M. , Hovland, G. , and Huynh, V. K. , 2014, “ Parameters Identification of Induction Motor Dynamic Model for Offshore Applications,” 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6.
Bose, B. K. , 2002, Power Electronics and AC Drives, Prentice Hall, Upper Saddle River, NJ.
Novotny, D. W. , and Lipo, T. A. , 1996, Vector Control and Dynamics of AC Drives, Oxford University Press, New York.
de Wit, C. C. , Olsson, H. , Åström, K. J. , and Lischinsky, P. , 1995, “ A New Model for Control of Systems With Friction,” IEEE Trans. Autom. Control, 40(3), pp. 419–425. [CrossRef]
Pawlus, W. , Choux, M. , Hovland, G. , Øydna, S. , and Hansen, M. R. , 2014, “ Modeling and Simulation of an Offshore Pipe Handling Machine,” SIMS 55th Conference on Simulation and Modeling, pp. 277–284.
Lian, K.-Y. , Hung, C.-Y. , Chiu, C.-S. , and Liu, P. , 2005, “ Induction Motor Control With Friction Compensation: An Approach of Virtual-Desired-Variable Synthesis,” IEEE Trans. Power Electron., 20(5), pp. 1066–1074. [CrossRef]
Gutierrez-Villalobos, J. , Rodriguez-Resendiz, J. , Rivas-Araiza, E. , and Mucino, V. , 2013, “ A Review of Parameter Estimators and Controllers for Induction Motors Based on Artificial Neural Networks,” Neurocomputing, 118, pp. 87–100. [CrossRef]
Pawlus, W. , Karimi, H. , and Robbersmyr, K. , 2010, “ Analysis of Vehicle to Pole Collision Models: Analytical Methods and Neural Networks,” Int. J. Control Theory Appl., 3(2), pp. 57–77.
Zhao, L. , Pawlus, W. , Karimi, H. , and Robbersmyr, K. , 2014, “ Data-Based Modeling of Vehicle Crash Using Adaptive Neural-Fuzzy Inference System,” IEEE/ASME Trans. Mechatronics, 19(2), pp. 684–696. [CrossRef]
Technosoft, 2011, DMCode-MS(IM) MATLAB Library for MCK28335.
Hinkkanen, M. , Repo, A.-K. , Ranta, M. , and Luomi, J. , 2010, “ Small-Signal Modeling of Mutual Saturation in Induction Machines,” IEEE Trans. Ind. Appl., 46(3), pp. 965–973. [CrossRef]
Bonchis, A. , Corke, P. , and Rye, D. , 2002, “ Experimental Evaluation of Position Control Methods for Hydraulic Systems,” IEEE Trans. Control Syst. Technol., 10(6), pp. 876–882. [CrossRef]
Aziz, M. , and Mohd-Mokhtar, R. , 2010, “ Performance Measure of Some Subspace-Based Methods for Closed-Loop System Identification,” 2010 Second International Conference on Computational Intelligence, Modeling and Simulation (CIMSiM), pp. 255–260.
Rouse, E. , Hargrove, L. , Perreault, E. , and Kuiken, T. , 2012, “ Estimation of Human Ankle Impedance During Walking Using the Perturberator Robot,” Fourth IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 373–378.

Figures

Grahic Jump Location
Fig. 1

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

Grahic Jump Location
Fig. 2

FOC of induction machine [14]

Grahic Jump Location
Fig. 3

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

Grahic Jump Location
Fig. 4

Structure of the neural network [14]

Grahic Jump Location
Fig. 5

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

Grahic Jump Location
Fig. 6

The gripper arm of MH VPR—courtesy of MHWirth

Grahic Jump Location
Fig. 7

A simplified gripper arm with the winch drivetrain [18]

Grahic Jump Location
Fig. 8

Test bench for running experiments

Grahic Jump Location
Fig. 9

Control system and interface diagram for induction motors

Grahic Jump Location
Fig. 10

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

Grahic Jump Location
Fig. 11

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

Grahic Jump Location
Fig. 12

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

Grahic Jump Location
Fig. 13

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

Grahic Jump Location
Fig. 14

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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In