Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion

Authors

  • Samuel R. Kingston Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA
  • Hunter Ellis Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA
  • Mashad U. Saleh Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA
  • Evan J. Benoit Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA
  • Ayobami Edun Department of Electrical and Computer Engineering University of Florida, Gainesville, FL, 32611, USA
  • Cynthia M. Furse Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA,
  • Michael A. Scarpulla Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA
  • Joel B. Harley Department of Electrical and Computer Engineering University of Florida, Gainesville, FL, 32611, USA

Keywords:

Spread Spectrum, Steepest Descent Inversion

Abstract

In this paper, we present a method for estimating complex impedances using reflectometry and a modified steepest descent inversion algorithm. We simulate spread spectrum time domain reflectometry (SSTDR), which can measure complex impedances on energized systems for an experimental setup with resistive and capacitive loads. A parametric function, which includes both a misfit function and stabilizer function, is created. The misfit function is a least squares estimate of how close the model data matches observed data. The stabilizer function prevents the steepest descent algorithm from becoming unstable and diverging. Steepest descent iteratively identifies the model parameters that minimize the parametric function. We validate the algorithm by correctly identifying the model parameters (capacitance and resistance) associated with simulated SSTDR data, with added 3 dB white Gaussian noise. With the stabilizer function, the steepest descent algorithm estimates of the model parameters are bounded within a specified range. The errors for capacitance (220pF to 820pF) and resistance (50 Ω to 270 Ω) are < 10%, corresponding to a complex impedance magnitude |R +1/jωC| of 53 Ω to 510 Ω.

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Author Biographies

Samuel R. Kingston, Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA

Samuel R. Kingston was born in Salt Lake City, Utah, UT, USA in 1991. He received the A.S. degree in Business from Salt Lake Community College, Salt Lake City, in 2011. He received a B.S. degree in Electrical & Computer Engineering from the University of Utah, Salt Lake City, UT, in 2016 and is currently working on a Ph.D. from the University of Utah, Salt Lake City, UT. From 2017 to current, he has been a Research Assistant with the University of Utah lab working in the algorithms group. He has been working with spread spectrum time domain reflectometry (SSTDR) in being able to detect, localize, and characterize faults within solar panel system. To date, he has written two conference papers and two journal papers for nondestructive health 196 ACES JOURNAL, Vol. 36, No. 2, February 2021 monitoring. He is currently working on several other journal papers as well, where each one will be a building block in achieving the overall research team goal. His research interests are in signal processing used for health monitoring, renewable energy alternatives, and creating successful startups from conceptual ideas. In 2016, his senior project team received an award for Best Clinic Project where he worked with L3 Communications to develop a way to detect low probability of intercept (LPI) RADAR signals.

Hunter Ellis, Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA

Hunter D. Ellis was born in Murray, Utah, UT, USA, in 1997. He is a current M.S. and B.S. student in Electrical Engineering at the University of Utah. He has been a Research Assistant with the University of Utah since 2018. His research interests include Spread Spectrum Time Domain Reflectometry, Photovoltaic, statistical signal processing, semiconductor physics, numerical simulation methods, adaptive filters, and antennas.

Mashad U. Saleh, Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA

Mashad Uddin Saleh (S’17-) received the B.Sc. Engineering degree in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh in 2013. He received the M.S. degree in Electrical Engineering from Michigan Technological University, Houghton, MI, USA in 2016. He is currently working towards the Ph.D. degree in Electrical Engineering at the University of Utah, Salt Lake City, UT, USA. He worked as a Research Assistant during his masters in Michigan Technological University and currently he is working as a Research Assistant at the University of Utah, Salt Lake City, UT. In summer 2018, he worked as a PV Electrical Characterization Intern in National Renewable Energy Laboratory (NREL). His current research interests include reliability, testing, manufacturing, measurements, and characterization of photovoltaic systems.

Evan J. Benoit, Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA

Evan J. Benoitreceived a B.S. degree in Nuclear Engineering Technology from Excelsior College in 2015. He received his B.S. and M.S. degrees in Electrical Engineering from the University of Utah in 2019. He is currently pursuing a Ph.D. in Electrical Engineering at the University of Utah. From 2005 to 2015, he was a Submarine Nuclear Field Electrician’s Mate in the US Navy. He began working as a Research Assistant at the University of Utah, Salt Lake City UT during the summer of 2018. His research explores the applicability of spread spectrum time domain reflectometry for identification of complex impedances on transmission lines. Benoit is a member of the Golden Key International Honour Society, the National Society of Leadership and Success, and a student member of IEEE.

Ayobami Edun, Department of Electrical and Computer Engineering University of Florida, Gainesville, FL, 32611, USA

Ayobami S. Edun received the B.Eng. degree in Electrical and Electronics Engineering from Federal University of Technology, Akure, Nigeria in 2014. He received the M.S. degree in Electrical and Computer Engineering from University of Florida, Gainesville, FL, USA in 2019. He is currently working towards the Ph.D. degree in Electrical and Computer Engineering at the University of Florida, Gainesville, FL, USA. He currently works as a Research Assistant at the SmartDATA Lab, University of Florida, Gainesville, FL. He has been working with spread spectrum time domain reflectometry (SSTDR) in being able to detect, localize, and characterize faults within solar panel system.

Cynthia M. Furse, Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA,

Cynthia M. Furse (M’85–SM’99– F’08) is Professor of Electrical and Computer Engineering at the University of Utah. Furse received her B.S. in Electrical Engineering with a Mathematics minor in 1985, M.S. degree in Electrical Engineering in 1988, and her Ph.D. in Electrical Engineering from the University of Utah in 1994. She has applied her expertise in electromagnetics to sensing and communication in complex lossy scattering media such as the human body, geophysical prospecting, ionospheric plasma, and aircraft wiring networks. She has taught electromagnetics, wireless communication, computational electromagnetics, microwave engineering, antenna design, and introductory electrical engineering and has been a leader in the development of the flipped classroom. Furse is a Fellow of the IEEE and the National Academy of Inventors. She is a past AdCom member for the IEEE AP Society and past chair of the IEEE AP Education Committee. She has received numerous teaching and research awards including the 2020 IEEE Chen To Tai Distinguished Educator Award. She is a founder of LiveWire Innovation, Inc., a spin-off company commercializing devices to locate intermittent faults on live wires.

Michael A. Scarpulla, Department of Electrical and Computer Engineering University of Utah, Salt Lake City, UT, 84112, USA

Michael A. Scarpulla (M'05-SM'14) earned the Sc.B. degree from Brown University in 2000 and the Ph.D. from UC Berkeley in 2006, both in Materials Science and Engineering. His Ph.D. work focused on laser processing of ion implanted compound semiconductors, carrier mediated ferromagnetism, and multiband semiconductors. From 2006-2008 he was a Postdoctoral Scholar at UC Santa Barbara working on epitaxial integration of rareearth pnictides with III-V semiconductors using MBE. Since joining the ECE and MSE faculties at University of Utah in 2008, he has worked in light trapping for photovoltaics, materials processing and characterization of chalcogenide thin film photovoltaics, reflectometry in photovoltaic systems, and defects in wide-bandgap semiconductors. His hobbies include skiing, climbing, and other mountain adventures.

Joel B. Harley, Department of Electrical and Computer Engineering University of Florida, Gainesville, FL, 32611, USA

Joel B. Harley (S'05-M'14) received his B.S. degree in Electrical Engineering from Tufts University in Medford, MA, USA. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University in Pittsburgh, PA, USA in 2011 and 2014, respectively. In 2018, he joined the University of Florida, where he is currently an Assistant Professor in the Department of Electrical and Computer Engineering. Previously, he was an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah. His research interests include integrating novel signal processing, machine learning, and data science methods for the analysis of waves and time-series data. Harley's awards and honors include 2020 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society Star Ambassador Award, a 2020 and 2018 Air Force Summer Faculty Fellowship, a 2017 Air Force Young Investigator Award, a 2014 Carnegie Mellon A. G. Jordan Award (for academic excellence and exceptional service to the community). He has published more than 90 technical journal and conference papers, including four best student papers. He is a student representative advisor for the IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society, a member of the IEEE Signal Processing Society, and a member of the Acoustical Society of America.

References

P. V. Nikitin, K. V. S. Rao, R. Martinez, and S. F. Lam, “Sensitivity and impedance measurements of UHF RFID chips,” IEEE Trans. Microw. Theory Tech., vol. 57, no. 5, pp. 1297-1302, 2009.

D. L. Corwin and S. M. Lesch, “Apparent soil electrical conductivity measurements in agriculture,” Comput. Electron. Agric., vol. 46, no. 1-3, pp. 11- 43, Mar. 2005. KINGSTON, ELLIS, SALEH, BENOIT, EDUN, FURSE, ET AL.: SPREAD SPECTRUM TDR AND STEEPEST DESCENT INVERSION 195

S. P. Bharadwaj, A. E. Ginart, I. N. Ali, P. W. Kalgren, J. R. Celaya, and S. D. Poll, “Solar cells aging estimation based on impedance characterization,” Aerospace Conference, pp. 1-9, Mar. 2011.

W. Stutzman and G. Thiele, Antenna Theory and Design. Wiley Global Education, 2012.

V. Schlosser and A. Ghitas, “Measurement of silicon solar cells AC parameters,” ARSEC, 2006.

P. A. Cotfas, D. T. Cotfas, P. N. Borza, D. Sera, and R. Teodorescu, “Solar cell capacitance determination based on an RLC resonant circuit,” Energies, vol. 11, no. 3, Art. no. 3, Mar. 2018.

R. A. Kumar, M. S. Suresh, and J. Nagaraju, “Facility to measure solar cell ac parameters using an impedance spectroscopy technique,” Rev. Sci. Instrum., vol. 72, no. 8, pp. 3422-3426, July 2001.

D. Rytting, “ARFTG 50 year network analyzer history,” in 2008 71st ARFTG Microwave Measurement Conference, pp. 1-8, 2008.

IET Labs, Inc., “Precision LCR Meter User and Service Manual,” IET Labs, 2014.

Anritsu, “Series 37XXXC Vector Network Analyzer Operations Manual,” Anritsu, 2004.

M. N. Akram and S. Lotfifard, “Modeling and health monitoring of DC side of photovoltaic array,” IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1245-1253, 2015.

J. Haney and A. Burstein, “PV system operations and maintenance fundamentals,” Next Phase Solar, Inc., Aug. 2013.

P. Smith, C. Furse, and J. Gunther, “Analysis of spread spectrum time domain reflectometry for wire fault location,” IEEE Sens. J., vol. 5, no. 6, pp. 1469-1478, Dec. 2005.

C. M. Furse, M. Kafal, R. Razzaghi, and Y. Shin, “Fault diagnosis for electrical systems and power networks: A review,” IEEE Sens. J., pp. 1-1, 2020.

H. Boudjefdjouf, H. R. E. H. Bouchekara, R. Mehasni, M. K. Smail, A. Orlandi, and F. de Paulis, “Wire fault diagnosis using time-domain reflectometry and backtracking search optimization algorithm,” in 31st International Review of Progress in ACES, pp. 1-2, 2015.

S. R. Kingston, N. K. T. Jayakumar, M. U. Saleh, E. J. Benoit, A. S. Edun, R. Sun, C. Furse, M. Scarpulla, and J. B. Harley, “Measurement of capacitance using spread spectrum time domain reflectometry (SSTDR) and dictionary matching,” IEEE Sens. J., pp. 1-1, 2020.

N. K. T. Jayakumar, E. J. Benoit, S. R. Kingston, M. U. Saleh, M. Scarpulla, J. B. Harley, and C. Furse, “Post-processing for improved accuracy and resolution of spread spectrum time domain reflectometry (SSTDR),” IEEE Sens. Lett., vol. 2, no. 3, 2019.

C. Furse, N. K. T. Jayakumar, E. J. Benoit, M. U. Saleh, J. LaCombe, M. Scarpulla, J. B. Harley, S. R. Kingston, B. Waddoups, and C. Deline, “Spread spectrum time domain reflectometry for complex impedances: Application to PV arrays,” IEEE AUTOTESTCON, pp. 1-4, 2018.

F. Cohen‐Tenoudji, B. R. Tittmann, and G. Quentin, “Technique for the inversion of backscattered elastic wave data to extract the geometrical parameters of defects with varying shape,” Appl. Phys. Lett., vol. 41, no. 6, pp. 574-576, Sep. 1982.

M. S. Zhdanov, S. Fang, and G. Hursán, “Electromagnetic inversion using quasi‐linear approximation,” GEOPHYSICS, vol. 65, no. 5, pp. 1501-1513, Sep. 2000.

Inverse Theory and Applications in Geophysics. 2nd ed., Elsevier, 2015.

M. El-Shenawee, C. Rappaport, D. Jiang, and W. Melsei, “Electromagnetics computations using the MPI parallel implementation of the steepest descent fast multipole method (SDFMM),” Applied Computational Electromagnetics Society, vol. 17, no. 2, 2002.

A. A. Goldstein, “On steepest descent,” J. Soc. Ind. Appl. Math. Ser. Control, vol. 3, no. 1, pp. 147-151, 1965.

M. U. Saleh, C. Deline, S. R. Kingston, N. K. T. Jayakumar, E. J. Benoit, J. B. Harley, C. Furse, and M. Scarpulla, “Detection and localization of disconnections in PV strings using spread-spectrum time-domain reflectometry,” IEEE J. Photovolt., vol. 10, no. 1, pp. 236-242, 2020.

R. Jacobs, “Increased rates of convergence through learning rate adaptation,” Neural Netw., vol. 1, pp. 295-307, 1988.

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Published

2021-02-01

How to Cite

[1]
Samuel R. Kingston, “Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion”, ACES Journal, vol. 36, no. 2, pp. 190–198, Feb. 2021.

Issue

2021: Vol 36 Iss 2

Section

General Submission