“Modeling, Identification and Control at Telemark University College” Authors: B. Lie, D. Di Ruscio, R. Ergon, B. Glemmestad, M. Halstensen, F. Haugen, S. Mylvaganam, N.-O. Skeie, D. Winkler Affiliation: Telemark University College (HiT) Reference: 2009, Vol. 30, No. 3, pp. 133-147.

Keywords: modeling, simulation, identification, control, sensor technology

Abstract: Master studies in process automation started in 1989 at what soon became Telemark University College, and the 20 year anniversary marks the start of our own PhD degree in Process, Energy and Automation Engineering. The paper gives an overview of research activities related to control engineering at Department of Electrical Engineering, Information Technology and Cybernetics.

PDF (1287 Kb)        DOI: 10.4173/mic.2009.3.4

DOI forward links to this article:
[1] Yan Ru Chaminda Pradeep Saba Mylvaganam, (2011), doi:10.1088/0957-0233/22/10/104006


References:
[1] Aaker, O. Operator Support and Diagnostic Reasoning in an Industrial Process. Ph.D. thesis, NTNU, Porsgrunn, 1996. NTNU-HiT, 82-7119-976-5, 0802-3271.
[2] Alic, M., Hauge, T. A., and Lie, B. Developing A Simple Modelica Library For Simulation of The Xstrata Nikkelverk Copper Production Plant. In Dong Energy, editor, Proc. 50th Scand. Conf. on Simulation and Modeling (SIMS). Fredericia, Denmark, 2009.
[3] Alme, K. J. Artificial Neural Network for Direct Parameter Estimation in Electrical Capacitance Tomography. In Protocom 2006. Warsawa, 2006.
[4] Alme, K. J. Material Distribution and Interface Detection Using EIT. Ph.D. thesis, NTNU-HiT, Porsgrunn, 2007. 2007:223, ISBN 978-82-471-4929-3 (printed), 978-82-471-4932-0 (elec.), ISSN 1503-8181.
[5] Alme, K. J. and Mylvaganam, S. Conductivity Effects in Electrical Tomography Systems with focus on 3D Effects Using COMSOL Multiphysics Modules. In Comsol Multiphysics Conf. Copenhagen, 2006a
[6] Alme, K. J. and Mylvaganam, S. Electrical Capacitance Tomography - Sensor Models, Design, Simulations and Experimental Verification. IEEE Sensors Journal, 2006b. 6(5):1256-1266. doi:10.1109/JSEN.2006.881409
[7] Alme, K. J. and Mylvaganam, S. Comparison of Different Measurement Protocols in Electrical Capacitance Tomography Using Simulations. IEEE Trans. on Instr. & Meas., 2007. 56(6):2119-2130
[8] Aoki, M. State Space Modeling of Time Series. Springer-Verlag Berlin Heidelberg, 1987
[9] Chai, Q. Modeling, Estimation, and Control of Biological Wastewater Treatment Plants. Ph.D. thesis, NTNU-HiT, Porsgrunn, 2008. 2008:108, ISBN 978-82-471-8155-3, 978-82-471-8169-0, ISSN 1503-8181.
[10] Chai, Q. and Lie, B. Predictive Control of an Intermittently Aerated Activated Sludge Process. American Control Conference, 2008. June:11-13. Seattle, USA.
[11] Dahlquist, E. Use of Modeling and Simulation in Pulp and Paper Industry. 2008. http://works.bepress.com/dr_erik_dahlquist. ISBN-977493-0-5
[12] Damslora, A. J. Optimisation Based Control of Batch Emulsion Polymerisation of Vinyl Chloride. Ph.D. thesis, NTNU-HiT, Porsgrunn, 1998. Thesis 1998:29, ISBN 82-471-0224-2.
[13] Damslora, A. J., Lie, B., and Sælid, S. Reduction of PVC Batch Time by Optimal Control of Free Radical Concentration. Nonlinear Model Based Control, 1998. 353:781-803. Kluwer, The Netherlands.
[14] Datta, U. Multimodal Measurements in Dilute Phase Pneumatic Conveying Systems. Ph.D. thesis, NTNU-HiT, 2007. 2007:240, ISBN 978-82-471-5322-2 (printed), 978-82-471-5336-9 (elec.), ISSN 1503-8181.
[15] Datta, U., Dyakowski, T., and Mylvaganam, S. Average Particle Size and Instantaneous Mass Flow Rate Estimation in Dilute Phase Pneumatic Conveying Systems using ECT. In 5th World Congress on Industrial Process Tomography. Bergen, 2007a
[16] Datta, U., Dyakowski, T., and Mylvaganam, S. Estimation of Particulate Velocity Components in Pneumatic Transport Using Pixel Based Correlation with Dual Plane ECT. Chemical Engineering Journal, 2007b. 130(2/3):87-99. doi:10.1016/j.cej.2006.08.034
[17] Datta, U., Mathiesen, V., and Mylvaganam, S. Tomometric Approach using Multi Sensor Data Fusion in Particle Segregation Studies. In 3rd World Cong. on Industrial Process Tomography. Banff, Canada, 2003
[18] Di Ruscio, D. Methods for the Identification of State Space Models from Input and Output Measurements. In SYSID 94, The 10th IFAC Symposium on System Identification. Copenhagen, 1994
[19] Di Ruscio, D. Combined Deterministic and Stochastic System Identification and Realization: DSR - a Subspace Approach Based on Observations. Modeling, Identification and Control, 1996. 17(3):193-230. doi:10.4173/mic.1996.3.3
[20] Di Ruscio, D. A Weighted View of the Partial Least Squares Algorithm. Automatica, 2000. 36:831-850. doi:10.1016/S0005-1098(99)00210-1
[21] Di Ruscio, D. Subspace System Identification of the Kalman Filter. Modeling, Identification and Control, 2003. 24(3):125-157. doi:10.4173/mic.2003.3.1
[22] Di Ruscio, D. Subspace System Identification of the Kalman Filter: Open and Closed Loop Systems. In Proc. of the Intl. Multi-Conference on Engineering and Technological Innovation. Orlando, USA, 2008
[23] Duenas Diez, M. Population Balance Modeling and Passivity-based Control of Particulate Processes, Applied to the Silgrain. Ph.D. thesis, NTNU-HiT, Porsgrunn, 2004. 2004:35, ISBN 82-471-6276-8 (printed), 82-471-6274-1 (elec.), ISSN 1503-8181.
[24] Duenas Diez, M., Fjeld, M., Andersen, E., and Lie, B. Validation of a Compartmental Population Balance Model of an Industrial Leaching Process: The Silgrain Process. Chemical Engineering Science, 2006. 61(1):229-245. doi:10.1016/j.ces.2005.01.047
[25] Duenas Diez, M., Ydstie, B. E., Fjeld, M., and Lie, B. Inventory Control of Particulate Processes. Computers and Chemical Engineering, 2008. 32:46-67. doi:10.1016/j.compchemeng.2007.01.007
[26] Ergon, R. Dynamic System Multivariate Calibration for Optimal Primary Output Estimation. Ph.D. thesis, NTNU, Trondheim, 1999a. 1999:72, NTNU-HiT, ISBN 82-471-0442-3, ISSN 0802-3271.
[27] Ergon, R. On primary output estimation by use of secondary measurements as input signals in system identification. IEEE Trans. Autom. Control, 1999b. 44(4):821-825. doi:10.1109/9.754826
[28] Ergon, R. Noise handling capabilities of multivariate calibration models. Modeling, Identification and Control, 2002a. 23:259-273. doi:10.4173/mic.2002.4.2
[29] Ergon, R. PLS score-loading correspondence and a biorthogonal factorization. J. Chemometrics, 2002b. 16(7):368-373. doi:10.1002/cem.736
[30] Ergon, R. Compression into two-component PLS factorization. J. Chemometrics, 2003. 17(6):303-312. doi:10.1002/cem.803
[31] Ergon, R. Informative PLS score-loading plots for process understanding and monitoring. Process Control, 2004. 14(8):889-897. doi:10.1016/j.jprocont.2004.02.004
[32] Ergon, R. PLS post-processing by similarity transformation (PLS+ST): A simple alternative to 0-PLS. Journal of Chemometrics, 2005. 19(1):1-4. doi:10.1002/cem.899
[33] Ergon, R. Reduced PCR/PLSR models by subspace projection. Chemom. Intell. Lab. Syst., 2006. 81(1):68-73. doi:10.1016/j.chemolab.2005.09.008
[34] Ergon, R. Finding Y-relevant part of X by use of PCR and PLSR model reduction methods. Journal of Chemometrics, 2007. 21:537-546. doi:10.1002/cem.1062
[35] Ergon, R. Informative score-loading-contribution plots for multi-response process monitoring. Chemom. Intell. Lab. Syst., 2009a. 95(1):31-34. doi:10.1016/j.chemolab.2008.08.001
[36] Ergon, R. Re-interpretation of NIPALS results solves PLSR inconsistency problem. Journal of Chemometrics, 2009b. 23:72-75. doi:10.1002/cem.803
[37] Ergon, R. and Di Ruscio, D. Dynamic system calibration by system identification methods. In Fourth European Control Conf. (ECC 97). Brussels, 1997
[38] Ergon, R. and Halstensen, M. Dynamic system multivariate calibration based on multirate sampling data. Modeling, Identification and Control, 2001. 22(2):73-88. doi:10.4173/mic.2001.2.2
[39] Esbensen, K. Multivariate Analysis - in practice. Camo AS, Oslo, 5th edition, 2001
[40] Esbensen, K. H., Halstensen, M., Lied, T. T., Saudland, A., Svalestuen, J., de Silva, S., and Hope, B. Acoustic chemometrics - from noise to information. Chemom. Intell. Lab. Sys., 1998. 44:61-76. doi:10.1016/S0169-7439(98)00114-2
[41] Esbensen, K. H., Hope, B., Lied, T. T., Halstensen, M., Sundberg, K., and Gravermoen, T. Acoustic chemometrics for fluid flow quantifications -II: A small constriction will go a long way. Journal of Chemometrics, 1999. 13:209-236
[42] Furenes, B. Model Based Control of Solidification. Ph.D. thesis, NTNU-HiT, 2009. 2009:228, ISBN-471-1860-3, 978-82-471-1861-0, ISSN 1503-8181
[43] Glemmestad, B. Optimal Operation of Integrated Processes, Studies on heat recovery systems. Ph.D. thesis, NTNU-HiT, 1997. 1997:118, ISBN 82-471-0156-4, ISSN 0802-3271
[44] Glemmestad, B., Ertler, G., and Hillestad, M. Advanced Process Control in a Borstar PP Plant. In Proc. from ECOREP II. Lyon, 2002 pages 46-49
[45] Glemmestad, B. and Hillestad, B. Experience from an Industrial MPC Implementation. In NPC10. NPC, Åbo, Finland, 2001 pages 77-78
[46] Glemmestad, B., Hofsten, K., Wilsher, E., and Andersen, K. S. Non-Linear Model Predictive Control of Polyolefin Plants. In NPC12. n/a, Gothenburg, Sweden, 2004 pages 66-67
[47] Golub, G. and Kahan, W. Calculating the singular values and pseudo-inverse of a matrix. SIAM J. Numer. Anal., 1965. 2:205-224
[48] Golub, G. H. and Van Loan, C. F. Matrix Computations. North Oxford Academic, London, 1986
[49] Halstensen, M. Acoustic Chemometrics. Experimental multivariate sensor technology and development of system prototypes for industrial multi-phase characterisation: selected forays. Ph.D. thesis, NTNU-HiT, 2001. 2001:111, ISBN 82-471-5381-5, ISSN 0809-103X
[50] Halstensen, M., de Bakker, P., and Esbensen, K. H. Acoustic chemometric monitoring of an industrial granulation production process - a PAT feasibility study. Chemom. Intell. Lab. Syst., 2006. 84:88-97. doi:10.1016/j.chemolab.2006.05.012
[51] Halstensen, M. and Esbensen, K. H. New developments in acoustic chemometric prediction of particle size distribution - the problem is the solution. Journal of Chemometrics, 2000. 14:463-481
[52] Halstensen, M., de Silva, S., and Esbensen, K. H. Acoustic monitoring of pneumatic transport lines - from noise to information. KONA Powder and Particle, 1998. 16:170-178
[53] Hauge, T. A. Roll-out of Model Based Control with Application to Paper Machines. Ph.D. thesis, NTNU, 2003. 2003:31, ISBN 82-471-5581-8, ISSN 0809-103X
[54] Hauge, T. A., Slora, R., and Lie, B. Application and roll-out of infinite horizon MPC employing a nonlinear mechanistic model to paper machines. Journal of Process Control, 2005. 15(2):201-213. doi:10.1016/j.jprocont.2004.05.003
[55] Haugen, F. Introduction to LabVIEW Control Design, System Identification and Simulation Tools. In National Instruments Days. Drammen, Norway, 2005
[56] Haugen, F. Examples of Student Assignments on Modeling, Simulation, and Control. In National Instruments Days. Drammen, Norway, 2008
[57] Haugen, F. Air Heater. Technical report, Telemark University College, 2009a. http://home.hit.no/~finnh/air_heater
[58] Haugen, F. Basic Dynamics and Control. TechTeach, 2009b. ISBN 978-82-91748-13-9
[59] Haugen, F. Lab assignment: Hardware-in-the-loop (HIL) simulation. 2009c. http://www2.hit.no/tf/fag/sce2006/2009/hil_sim
[60] Haugen, F. Lab assignment: Implementation of a control system. 2009d. http://www2.hit.no/tf/fag/sce1106/2009/project/project
[61] Haugen, F. Lab assignment: Model-based predictive control (MPC). 2009e. http://www2.hit.no/tf/fag/scev3106/2009/projects/mpc
[62] Haugen, F. Lab assignment: Soft-sensor (state estimator). 2009f. http://www2.hit.no/tf/fag/sce4206/2009/softsensor
[63] Haugen, F. Lab assignment: System identification. 2009g. http://www2.hit.no/tf/fag/sce4206/2009/system_ident
[64] Haugen, F. Lecture Notes in Models, Estimation and Control. TechTeach, 2009h. ISBN 978-82-91748-14-6
[65] Haugen, F. Water Tank. Technical report, Telemark University College, 2009i. http://home.hit.no/~finnh/dok_tankmodell
[66] Haugen, F., Fjelddalen, E., Edgar, T., and Dunia, R. Examples of Student Assignments on Modeling, Simulation, and Control. CACHE News (Computer Aids for Chemical Engineering), 2007. Winter
[67] Haugen, F., Fjelddalen, E., Edgar, T., and R. Dunia. A Complete Programming Framework for Process Control Education. In 2nd IEEE Multi-conference on Systems and Control. IEEE, San Antonio, 2008
[68] Haugwitz, S., Wilsher, E., Hofsten, K., Andersen, K. S., and Glemmestad, B. Commissioning of Nonlinear Model Predictive Controllers to a New-Polypropylene Plant. In Reglermötet. Sweden, 2008
[69] Hestenes, M. R. and Stiefel, E. Methods for Conjugate Gradients for Solving Linear Systems. J. of Res. of the national Bureau of Stand., 1952. 49(6):409-436
[70] Ho, B. L. and Kalman, R. E. Effective construction of linear state-variable models from input/output functions. Regelungstechnik, 1966. 14(12):545-592
[71] Huang, J. Developments in Applied Chemometrics AMT, acoustic chemometrics and N-way image analysis. Ph.D. thesis, NTNU-HiT, Porsgrunn, 2001. 2001:25, ISBN 82-7984-187-3, ISSN 0809-103X
[72] Komperød, M., Bones, J. A., and Lie, B. Solution to an Implementation Issue for a Two-Step ARX Algorithm, with Application to the Czochralski Crystallization Process. In Proc. 50th Scand. Conf. on Simulation and Modeling. Fredericia, 2009
[73] Lie, B. Control Structures for Polymerization Processes Applied to Polypropene Manufacturing. Ph.D. thesis, NTNU, Trondheim, 1990. Report 82-W
[74] Lie, B. Attainable Performance in LQG Control. Methods of Model Based Process Control, 1995. 293:263-295. Kluwer Academic Publishers.
[75] Lie, B. Model Uncertainty and Control Consequences: a Paper Machine Study. Mathematical and Computer Modelling of Dynamical Systems, 2009. 15(5):463-477. doi:10.1080/13873950903375452
[76] Lie, B., Duenas Diez, M., and Hauge, T. A. A Comparison of Implementation Strategies for MPC. Modeling, Identification and Control, 2005. 26(1):39-50. doi:10.4173/mic.2005.1.3
[77] Lie, B. and Hauge, T. A. Modeling of an Industrial Copper Leaching and Electrowinning Process, with Validation Against Experimental Data. In Proc. 49th Scand. Conf. on Simulation & Modeling. Oslo, 2008
[78] Lie, B. and Heath, W. Model Based Control. In E. Dahlquist, editor, Use of Modeling and Simulation in Pulp and Paper Industry. 2008 pages 978-91. http://works.bepress.com/dr_erik_dahlquist. COST Office, ISBN-977493-0-5
[79] Lied, T. T. Multivariate Image Regression (MIR) for Quantitative Predictions. Ph.D. thesis, NTNU-HiT, 2000. 2000:99, ISBN 82-7984-126-1, ISSN 0809-103X
[80] Lorentzen, H., Timmerberg, J., and Mylvaganam, S. Calculation of Cable Parameters for Different Cable Shapes. In European COMSOL Conf. 2008
[81] Lundhaug, M. Sea Ice Studies in the Northern Sea Route by Use of Synthetic Aperture Radar. Ph.D. thesis, NTNU-HiT, Porsgrunn, 2002. 2002:22, ISBN 82-471-5414-5, ISSN 0809-103X
[82] Martens, H. and Næs, T. Multivariate Calibration. John Wiley and Sons Ltd, 1989
[83] Modelica-Association. Modelica - A Unified Object-Oriented Language for Physical Systems Modeling - Language Specification. Modelica Association, version 3.1 edition, 2009. http://www.modelica.org
[84] Modelica.org. Tools page on Modelica.org. 2009. http://www.modelica.org/tools
[85] Mylvaganam, S. Some Applications of Acoustic Emission in Particle Science and Technology. Particulate Science and Technology, 2003. 21(3):293-301. doi:10.1080/02726350307485
[86] Mylvaganam, S., Datta, U., Halstensen, M., and Mathiesen, V. Multi Sensor Suite Comprising Active and Passive Transducers for Monitoring Solids Flow and Silo Integrity. In IEEE UFFC Symp. Honolulu, 2003
[87] Mylvaganam, S. and Dyakowski, T. Estimating transverse velocity components of slug flow using pixel by pixel correlation method using ECT. In 4th World Cong. Ind. Proc. Tomography. Aizu, 2005 pages 5-8
[88] Nilsen, G. W. Topics in Open and Closed Loop Subspace System Identification: Finite Databased Methods. Ph.D. thesis, NTNU-HiT, 2005. 2005:228, ISBN-7357-3, 82-471-7356-5, ISSN 1503-8181
[89] Nygaard, G., Nævdal, G., and Mylvaganam, S. Evaluating Nonlinear Kalman Filters for Parameter Estimation in Reservoirs During Petroleum Well Drilling. In IEEE Conference on Control Applications. München, Germany, 2006 pages 4-6
[90] Nygaard, G. H. and Nævdal, G. Non-linear model predictive control scheme for stabilizing annulus pressure during oil well drilling. Journal of Process Control, 2006. 16(7):719-732. doi:10.1016/j.jprocont.2006.01.002
[91] Nygaard, G. H., Vefring, E. H., Fjelde, K.-K., Nævdal, G., Lorentzen, R. J., and Mylvaganam, S. Bottomhole Pressure Control during Drilling Operations in Gas-Dominant Wells. SPE Journal, 2007. 12(1):49-61. doi:10.2118/91578-PA
[92] Nygaard, G. H., Vefring, E. H., Lorentzen, R. J., Nævdal, G., Fjelde, K. K., and Mylvaganam, S. Bottomhole pressure control during pipe connection in gas-dominant wells. In SPE/IADC Underbalanced Tech. Conf. and Exhibition. Houston, 2004a
[93] Nygaard, G. H., Vefring, E. H., Mylvaganam, S., Lorentzen, R. J., Nævdal, G., and Fjelde, K. K. Underbalanced drilling: improving pipe connection procedures using automatic control. In SPE Ann. Tech. Conf. and Exh. Conv. Center, Houston, 2004b
[94] Nygaard, O. G. H. Multivariable Process Control in High Temperature and High Pressure Environment Using Non-intrusive Multi Sensor Data Fusion. Ph.D. thesis, NTNU-HiT, 2006. 2006:38, ISBN 82-471-7820-6, 82-471-7819-2, ISSN 1503-8181
[95] Pell, R., Ramos, L., and Manne, R. The model space in partial least squares regression. Journal of Chemometrics, 2007. 21:165-172. doi:10.1002/cem.1067
[96] Qin, S. J. and Ljung, L. Closed-loop Subspace Identification with Innovation Estimation. In Proc. of the 13th IFAC SYSID Symposium. 2003 pages 887-892
[97] Qin, S. J., Weilu, L., and Ljung, L. A Novel Subspace Approach with Enforced Causal Models. Automatica, 2005. 41:2043-2053. doi:10.1016/j.automatica.2005.06.010
[98] Sarmiento Ferrero, C., Chai, Q., Duenas Diez, M., Amrani, S. H., and Lie, B. Systematic Analysis of Parameter Identifiability for Improved Fitting of a Biological Wastewater Model to Experimental Data. Modeling, Identification and Control, 2006. 27(4):219-238. doi:10.4173/mic.2006.4.2
[99] Skeie, N.-O. Soft Sensors for Level Estimation. Ph.D. thesis, NTNU-HiT, Porsgrunn, 2008. 2008:102, ISBN 978-82-471-4462-6, 978-82-471-4476-3, ISSN 1503-8181
[100] Skeie, N.-O. and Lie, B. Level and Interface Estimation in an Oil / Water Separator Using Multi Sensor Data Fusion, in E. In E. Juuso, editor, 47th Conf. on Simulation and Modelling. 2006 pages 210-215
[101] Skeie, N.-O., Mylvaganam, S., and Lie, B. Using Multi Sensor Data Fusion for Level Estimation in a Separator, in W. In 16th European Symp. on Comp. Aided Proc. Engn. and 9th Intl. Symp. on Proc. Sys. Engn., volume 21. Elsevier, 2006 pages 1383-1388
[102] Sotomayor, O. A. Z., Parka, S. W., and Garcia, C. Multivariable Identification of an Activated Sludge Process with Subspace-based Algorithms. Control Engineering Practice, 2003. 11:961-969. doi:10.1016/S0967-0661(02)00210-1
[103] Timmerberg, J., Beckmann, P., and Mylvaganam, S. Quasianalytical Estimates of Inductance Using Subconductor Methods. In Intern. Conference: Environment, Technology, Resources. Rezekne, Latvia, 2009
[104] Trygg, J. and Wold, S. Orthogonal projections to latent structures. O-PLS. J. Chemometrics, 2002. 16:119-128. doi:10.1002/cem.695
[105] Vefring, E. H., Nygaard, G. H., Fjelde, K. K., Rolf, Lorentzen, J., Nævdal, G., and Merl, A. Reservoir Characterization During Underbalanced Drilling: Methodology, Accuracy, and Necessary Data. In Proc. SPE Ann. Tech. Conf. San Antonio, 2002
[106] Videla, J. I. and Lie, B. A New Energy Building Simulation Library. In C. Kral and A. Haumer, editors, Proc. of the 5th Intl. Modelica Conference. The Modelica Association, Vienna, 2006 pages 685-693
[107] Videla, J. I. and Lie, B. Model Reduction of a Micro ICE Based CHP Model. In 1st Intl. Conf./Works. in Micro-Cogeneration Techn. and Appl. 2008
[108] Viumdal, H., Ru, B., Y.and Moxnes, and Liane, S., M. an Mylvaganam. Multi sensor data fusion for aluminium cell health monitoring and control. In TMS annual meeting Proceedings CD. Seattle, 2010
[109] Wærstad, H., Cortvriend, L., Datta, U., Mathiesen, V., and Mylvaganam, S. Multi Sensor Data Fusion With Flange Mounted Acoustic Emission Sensors In The Monitoring Of Fluidised Beds. In IEEE UFFC Symposium. Munich, Germany, 2002
[110] Winkler, D. and Gühmann, C. Simulation of Electric Drives using freeFOClib. In Proc. of IEEE Intl. Conf. on Sustainable Energy Tech. IEEE, SMU Conf. Centre, Singapore, 2008 doi:10.1109/ICSET.2008.4747173
[111] Winkler, D. and Gühmann, C. Simulation of Faults in Electric Drive Systems with Modelica. In Proc. MATHMOD 09 Vienna. 2009
[112] Yahoui, A., Mouhoumed, B., Ducharne, B., Mylvaganam, S., and Sixdenier, F. Diagnostic of interturn defect in three phase system by studying hysteresis magnetic harmonics signatures. In Proc. ICEM 2004, XVI Intl. Conf. On Electrical Mach. 2004
[113] Yahoui, H. and Mylvaganam, S. Modelling and characterisation of hysteresis phenomena in 3-phase power systems for diagnostic purposes. In Symp. on Diag. for Electrical Mach., Power Elec. and Drives. 2009
[114] Zeiger, H. and McEwen, A. Approximate Linear Realizations of Given Dimensions Via Ho's Algorithm. In IEEE Trans. on Automatic Control. 1974, doi:10.1109/TAC.1974.1100525


BibTeX:
@article{MIC-2009-3-4,
  title={{Modeling, Identification and Control at Telemark University College}},
  author={B. Lie and D. {Di Ruscio} and R. Ergon and B. Glemmestad and M. Halstensen and F. Haugen and S. Mylvaganam and N. -O. Skeie and D. Winkler},
  journal={Modeling, Identification and Control},
  volume={30},
  number={3},
  pages={133--147},
  year={2009},
  doi={10.4173/mic.2009.3.4},
  publisher={Norwegian Society of Automatic Control}
};