Metodología de estimación de parámetros en dispositivos eléctricos con procesos iterativos de simulación usando algoritmos genéticos

dc.contributorUstariz Farfán, Armando Jaime
dc.contributorGuerrero Guerrero, Andrés Felipe
dc.contributorGrupo de Investigación en Calidad de la Energía y Electrónica de Potencia
dc.creatorCastiblanco Pasuy, Johan Lisandro
dc.date.accessioned2021-07-02T21:27:34Z
dc.date.accessioned2022-09-21T15:35:57Z
dc.date.available2021-07-02T21:27:34Z
dc.date.available2022-09-21T15:35:57Z
dc.date.created2021-07-02T21:27:34Z
dc.date.issued2021
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/79762
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3382539
dc.description.abstractLa tesis se presenta como una metodología para realizar estimación de parámetros de dispositivos eléctricos por medio de simuladores electrónicos. Se aborda una alternativa a los problemas de manipulación de parámetros precisamente en la estimación de parámetros de modelo circuital de cargas eléctricas. De esa forma se propone una metodología que utiliza técnicas de inteligencia artificial como los algoritmos genéticos para validar modelos propuestos de softwares de simulación eléctrica. Así se encuentra una alternativa a la típica estimación de parámetros basada en funciones objetivo realizada sobre ecuaciones matemáticas de todo el sistema. Se realiza una contextualización respecto a la temática de estimación y el funcionamiento de las herramientas de simulación temporal de dispositivos eléctricos. Se presentan la elección de software, selección de herramientas, estructura de la metodología, su funcionamiento y el trabajo conjunto de estas para llevar a cabo la estimación. El software de simulación base es LTspice con uso del motor Spice para manipular parámetros eléctricos por medio de arreglo Netlist matricial. Se utiliza algoritmos genéticos y se presenta la función objetivo general para realizar la estimación en distintos escenarios. Se expone una manera de adquisición de señales para agrupar las señales por medio del dispositivo Analog Discovery 2 y se ejecuta todo el código principal de la metodología haciendo uso del lenguaje de programación Python. Finalmente, se valida la herramienta con 3 escenarios de estimación de dispositivos del área de ingeniería eléctrica haciendo estimación de parámetros de un transformador, un módulo fotovoltaico y una lampara fluorescente (CFL). La metodología agilizará el proceso involucrado para llevar estimación de parámetros a partir de datos adquiridos. Adicionalmente, brinda un método versátil para que los ingenieros, haciendo uso de conocimiento de simulación, realicen caracterización de los dispositivos eléctricos que necesiten de manera rápida sin modelamientos matemáticos- analíticos extensos y complejos.
dc.description.abstractThe Thesis is presented as a methodology to do parameter estimation of electric devices using electronic simulators. It approaches an alternative to problem of manipulation parameters in the parameter estimation of circuital models of electric loads. Is proposed a methodology that use artificial intelligent techniques like genetic algorithm to validate model proposed of electrical simulation software’s. In that way is founded and alternative to the typic techniques of parameter estimation bases of objective functions made it over mathematical equations of all the system. Is realized a contextualization about estimation topics, and the operation of simulation software tools focus on electrical devices in time analysis. Is presented the software choices, tools selection, structure of the methodology, its functioning, and the work in group of them to carry out the estimation. The base software for simulation is LTspice using the Spice motor to manipulate electrical parameters via Netlist matrixial configuration. Is used genetic algorithms and is presented the general objective function to make signals comparison to realize the estimation. Is exposed way to do signal acquisition to a group the signals via Analog Discovery device and it is executed all the principal code of methodology making use of Python programming language. Finally, is validated the tool with 3 scenarios of devices estimation in the electrical engineer area doing estimation of parameters of a transformer, photovoltaic module, and compact fluorescent lamp. The methodology shall agile the involved process to do parameter estimation bases on acquired data. Give a versatile method for engineers, that making use of simulation knowledge, they should realize electrical device characterization that can need in a fast way without making complex and large mathematical-analytic models.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherManizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Ingeniería Eléctrica
dc.publisherDepartamento de Ingeniería Eléctrica y Electrónica
dc.publisherFacultad de Ingeniería y Arquitectura
dc.publisherManizales, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Manizales
dc.relation[1]F. M. Posada, “La Politica de Importación Tecnológica en Colombia,” vol. 3, no. 4, pp. 539–570, 1979. [2]“Información estadística importaciones: Importaciones en Colombia subieron 3,9% según informe del Dane | Centro Virtual de Negocios - CVN.” [Online]. Available: https://www.cvn.com.co/informacion-estadistica-importaciones-colombia/. [Accessed: 28-Oct-2020]. [3]B. De Sucesos and Y. Estadísticas, “INFORME SECTORIAL SOBRE LA EVOLUCIÓN DE LA DISTRIBUCIÓN Y COMERCIALIZACIÓN DE ENERGÍA ELÉCTRICA EN COLOMBIA Ministerio de Minas y Energía República de Colombia,” 1998. [4]“Invierta y Gane con Energía Guía práctica para la aplicación de los incentivos tributarios de la Ley 1715 de 2014.” [5]“Energías alternativas se toman Colombia.” [Online]. Available: https://sostenibilidad.semana.com/medio-ambiente/articulo/energias-alternativas-se-toman-colombia/37756. [Accessed: 28-Oct-2020]. [6]J. C. Spall, Introduction to stochastic search and optimization: estimation, simulation, and control, vol. 65. John Wiley & Sons, 2005. [7]S. Andradottir, “Review of simulation optimization techniques,” in Winter Simulation Conference Proceedings, 1998, vol. 1, pp. 151–158, doi: 10.1109/wsc.1998.744910. [8]“A quick look at Julia, R and Python - knn Example | Towards Data Science.” [Online]. Available: https://towardsdatascience.com/julia-r-and-python-7cd50c2b0fe4. [Accessed: 28-Oct-2020]. [9]J. Blank and K. Deb, “Pymoo: Multi-Objective Optimization in Python,” IEEE Access, vol. 8, pp. 89497–89509, 2020, doi: 10.1109/ACCESS.2020.2990567. [10]M. Fathi and H. Bevrani, Optimization in Electrical Engineering. Springer International Publishing, 2019. [11]J. Roberts, K. Demarest, and G. Prescott, “What is electrical engineering today and what is it likely to become?,” in Proceedings - Frontiers in Education Conference, FIE, 2008, doi: 10.1109/FIE.2008.4720588. [12]A. F. Guerrero-Guerrero, J. L. Castiblanco-Pasuy, A. J. Ustariz-Farfan, and E. A. Cano-Plata, “Characterization of Parameters of a Non-dissipative Snubber Network Using a Genetic Algorithm,” in 2019 IEEE Workshop on Power Electronics and Power Quality Applications, PEPQA 2019 - Proceedings, 2019, doi: 10.1109/PEPQA.2019.8851548. [13]H. A. Toliyat, E. Levi, and M. Raina, “A Review of RFO Induction Motor Parameter Estimation Techniques,” IEEE Power Eng. Rev., vol. 22, no. 7, p. 52, 2002, doi: 10.1109/MPER.2002.4312369. [14]J. Sun, J. M. Garibaldi, and C. Hodgman, “Parameter estimation using metaheuristics in systems biology: A comprehensive review,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 9, no. 1, pp. 185–202, 2012, doi: 10.1109/TCBB.2011.63. [15]A. R. Jordehi, “Parameter estimation of solar photovoltaic (PV) cells: A review,” Renew. Sustain. Energy Rev., vol. 61, pp. 354–371, 2016, doi: 10.1016/j.rser.2016.03.049. [16]C. G. Moles, P. Mendes, and J. R. Banga, “Parameter estimation in biochemical pathways: A comparison of global optimization methods,” Genome Research, vol. 13, no. 11. Genome Res, pp. 2467–2474, Nov-2003, doi: 10.1101/gr.1262503. [17]D. Matajira-Rueda, J. Cruz Duarte, J. Aviña Cervantes, and C. Correa Cely, “Global optimization algorithms applied in a parameter estimation strategy,” Rev. UIS Ing., vol. 13, no. 1, pp. 233–242, 2018, doi: 10.18273/revuin.v17n1-2018023. [18]M. V. Liashov, N. N. Prokopenko, A. A. Ignashin, O. V. Dvornikov, and A. A. Zhuk, “Parametric optimization subsystem in ltspice environment of analog microcircuits for operation at low temperatures,” in 2019 IEEE East-West Design and Test Symposium, EWDTS 2019, 2019, doi: 10.1109/EWDTS.2019.8884446. [19] N. N. Prokopenko, M. V. Liashov, A. V. Bugakova, and A. A. Zhuk, “The Multi-Criteria Optimization in the LTspice Simulation Software of a JFet class AB Buffer Amplifier for Operation at Low Temperatures,” in Proceedings of the 2019 IEEE International Conference on Electrical Engineering and Photonics, EExPolytech 2019, 2019, pp. 21–24, doi: 10.1109/EExPolytech.2019.8906871. [20]D. Shleifman, R. H. Griffin, A. Dadvand, and T. Y. Chu, “Generic Parameter Extraction of Inkjet-Printed OTFTs via Optimisation Using LTspice and MATLAB,” 2018 Int. Flex. Electron. Technol. Conf. IFETC 2018, pp. 1–3, 2018, doi: 10.1109/IFETC.2018.8583889. [21]J. L. Castiblanco-Pasuy, “johanv26/python_ltspice_GA_estimation: Working repository of estimation tool for electrical devices based on GA.” [Online]. Available: https://github.com/johanv26/python_ltspice_GA_estimation. [Accessed: 03-Feb-2021]. [22]J. V. Beck and K. J. Arnold, Parameter estimation in engineering and science. James Beck, 1977. [23]D. G. Luenberger and Y. Ye, Linear and Nonlinear Programming, vol. 116. New York, NY: Springer US, 2008. [24]“Linear Programming and Network Flows - Mokhtar S. Bazaraa, John J. Jarvis, Hanif D. Sherali - Google Libros.” [Online]. Available: https://books.google.com.co/books/about/Linear_Programming_and_Network_Flows.html?id=2DKKHvV_xVwC&redir_esc=y. [Accessed: 29-Oct-2020]. [25]D. P. Bertsekas, “Nonlinear Programming,” J. Oper. Res. Soc., vol. 48, no. 3, pp. 334–334, Mar. 1997, doi: 10.1057/palgrave.jors.2600425. [26]H. Salkin, Foundations of integer programming. New York: North-Holland, 1989. [27]“Integer Programming | Wiley.” [Online]. Available: https://www.wiley.com/en-us/Integer+Programming-p-9780471283669. [Accessed: 28-Oct-2020]. [28]S. Boyd and L. Vandenberghe, Convex Optimization. . [29]E. L. Peterson, “Geometric Programming,” in Advances in Geometric Programming, Springer US, 1980, pp. 31–94. [30]A. I. Galushkin, Neural network theory. Springer Berlin Heidelberg, 2007. [31]J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968. [32]F. Rothlauf, “Representations for Genetic and Evolutionary Algorithms,” in Representations for Genetic and Evolutionary Algorithms, Springer Berlin Heidelberg, 2006, pp. 9–32. [33]M. Bakr, NONLINEAR OPTIMIZATION IN ELECTRICAL ENGINEERING WITH APPLICATIONS IN MATLAB®. . [34]J. A. Taylor, Convex Optimization of Power Systems. Cambridge University Press, 2015. [35]S. L. Sabat, K. S. Kumar, and S. K. Udgata, “Differential evolution and swarm intelligence techniques for analog circuit synthesis,” in 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings, 2009, pp. 469–474, doi: 10.1109/NABIC.2009.5393356. [36]G. Alpaydin, S. Balkir, and G. Dündar, “An evolutionary approach to automatic synthesis of high-performance analog integrated circuits,” IEEE Trans. Evol. Comput., vol. 7, no. 3, pp. 240–252, Jun. 2003, doi: 10.1109/TEVC.2003.808914. [37]“Fundamentals of optimization techniques in analog IC sizing,” in Studies in Computational Intelligence, vol. 501, Springer Verlag, 2014, pp. 19–40. [38]D. Dvorak, T. Bauml, A. Holzinger, and H. Popp, “A Comprehensive Algorithm for Estimating Lithium-Ion Battery Parameters from Measurements,” IEEE Trans. Sustain. Energy, vol. 9, no. 2, pp. 771–779, 2018, doi: 10.1109/TSTE.2017.2761406. [39]J. C. Gelvez and A. Ramirez, “Measurement-based characterization of residential lighting devices: CFL and LED,” 2017 North Am. Power Symp. NAPS 2017, 2017, doi: 10.1109/NAPS.2017.8107190. [40]M. W. Cohen, M. Aga, and T. Weinberg, “Genetic algorithm software system for analog circuit design,” in Procedia CIRP, 2015, vol. 36, pp. 17–22, doi: 10.1016/j.procir.2015.01.033. [41]E. Tlelo-Cuautle et al., “Applications of evolutionary algorithms in the design automation of analog integrated circuits,” J. Appl. Sci., vol. 10, no. 17, pp. 1859–1872, 2010, doi: 10.3923/jas.2010.1859.1872. [42]“Parameter Estimation - an overview | ScienceDirect Topics.” [Online]. Available: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/parameter-estimation. [Accessed: 29-Oct-2020]. [43]M. S. Rafaq and J. W. Jung, “A Comprehensive Review of State-of-the-Art Parameter Estimation Techniques for Permanent Magnet Synchronous Motors in Wide Speed Range,” IEEE Trans. Ind. Informatics, vol. 16, no. 7, pp. 4747–4758, 2020, doi: 10.1109/TII.2019.2944413. [44]W. MohamedAly, “Analog Electric Circuits Synthesis using a Genetic Algorithm Approach,” Int. J. Comput. Appl., vol. 121, no. 4, pp. 28–32, Jul. 2015, doi: 10.5120/21530-4523. [45]D. B. Fogel, “The Advantages of Evolutionary Computation.,” in BCEC, 1997, pp. 1–11. [46]P. Ponce Cruz, Inteligencia artificial con aplicaciones a la ingeniería. Alfaomega, 2011. [47]“Analog Discovery 2 [Digilent Documentation].” [Online]. Available: https://reference.digilentinc.com/reference/instrumentation/analog-discovery-2/start. [Accessed: 02-Feb-2021]. [48]A. V Oppenheim, R. W. Schafer, and J. R. Buck, “Tratamiento de señales en tiempo discreto,” 2011. [49]R. P. Areny, Adquisición y distribución de señales. Marcombo, 1993. [50]J. G. Proakis and D. G. Manolakis, “Digital signal processing,” PHI Publ. New Delhi, India, 2004. [51]“LTspice | Design Center | Analog Devices.” [Online]. Available: https://www.analog.com/en/design-center/design-tools-and-calculators/ltspice-simulator.html#. [Accessed: 02-Feb-2021]. [52]M. L. González, LTspice. Editorial de la Universidad Nacional de La Plata (EDULP), 2020. [53]M. Engelhardt, “SPICE Differentiation,” LT J. Analog Innov., no. January, pp. 10–16, 2015. [54]A. Vladimirescu, The SPICE book. Wiley New York, 1994. [55]H. W. Dommel, “Techniques for analyzing electromagnetic transients,” IEEE Comput. Appl. Power, vol. 10, no. 3, pp. 18–21, Jul. 1997, doi: 10.1109/67.595285. [56]Q. I. Rahman and G. Schmeisser, “Characterization of the speed of convergence of the trapezoidal rule,” Numer. Math., vol. 57, no. 1, pp. 123–138, 1990. [57]C. W. Gear, “Initial value problems: practical theoretical developments,” Mar. 1979. [58]C. A. Thompson, “A STUDY OF NUMERICAL INTEGRATION TECHNIQUES FOR USE IN THE COMPANION CIRCUlT METHOD OF TRANSIENT CIRCUIT ANALYSIS.” [59]T. P. Santamaría, Electrónica digital. Prensas Universitarias de Zaragoza, 1994. [60]“History and License — Python 3.9.1 documentation.” [Online]. Available: https://docs.python.org/3/license.html. [Accessed: 02-Feb-2021]. [61]W. McKinney, “pandas: a foundational Python library for data analysis and statistics,” Python High Perform. Sci. Comput., vol. 14, no. 9, 2011. [62]“ltspice · PyPI.” [Online]. Available: https://pypi.org/project/ltspice/. [Accessed: 31-Oct-2020]. [63]I. L. Spigariol, “Fundamentos teóricos de los Paradigmas de Programación Buenos Aires-Abril 2005.” [64]“Measuring API Usability | Dr Dobb’s.” [Online]. Available: https://www.drdobbs.com/windows/measuring-api-usability/184405654. [Accessed: 02-Feb-2021]. [65]M. Despotovic, V. Nedic, D. Despotovic, and S. Cvetanovic, “Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation,” Renew. Sustain. Energy Rev., vol. 56, pp. 246–260, 2016, doi: 10.1016/j.rser.2015.11.058. [66]D. / Oe and / Iser, “Large Power Transformers and the U.S. Electric Grid FOR FURTHER INFORMATION,” 2014. [67]C. E. Lin, J. M. Ling, and C. L. Huang, “An expert system for transformer fault diagnosis using dissolved gas analysis,” IEEE Trans. Power Deliv., vol. 8, no. 1, pp. 231–238, 1993, doi: 10.1109/61.180341. [68]L. Niemeyer, “A Generalized Approach to Partial Discharge Modeling,” IEEE Trans. Dielectr. Electr. Insul., vol. 2, no. 4, pp. 510–528, 1995, doi: 10.1109/94.407017. [69]Z. Zhang, N. Kang, and M. J. Mousavi, “Real-time transformer parameter estimation using terminal measurements,” IEEE Power Energy Soc. Gen. Meet., vol. 2015-Septe, pp. 1–5, 2015, doi: 10.1109/PESGM.2015.7285958. [70]A. McEvoy, L. Castaner, and T. Markvart, Solar cells: materials, manufacture and operation. Academic Press, 2012. [71]Z.-S. Li, G.-Q. Zhang, D.-M. Li, J. Zhou, L.-J. Li, and L.-X. Li, “Application and development of solar energy in building industry and its prospects in China,” Energy Policy, vol. 35, no. 8, pp. 4121–4127, 2007. [72]P. M. Cuce and E. Cuce, “A novel model of photovoltaic modules for parameter estimation and thermodynamic assessment,” Int. J. Low-Carbon Technol., vol. 7, no. 2, pp. 159–165, 2012, doi: 10.1093/ijlct/ctr034. [73]F. F. Muhammad, A. W. Karim Sangawi, S. Hashim, S. K. Ghoshal, I. K. Abdullah, and S. S. Hameed, “Simple and efficient estimation of photovoltaic cells and modules parameters using approximation and correction technique,” PLoS One, vol. 14, no. 5, May 2019, doi: 10.1371/journal.pone.0216201. [74]S. A. Afghan, H. Almusawi, and H. Geza, “Simulating the electrical characteristics of a photovoltaic cell based on a single-diode equivalent circuit model,” MATEC Web Conf., vol. 126, pp. 1–6, 2017, doi: 10.1051/matecconf/201712603002. [75]Z. Wei, N. R. Watson, and L. P. Frater, “Modelling of compact fluorescent lamps,” in ICHQP 2008: 13th International Conference on Harmonics and Quality of Power, 2008, doi: 10.1109/ICHQP.2008.4668833. [76]R. A. Jabbar, M. al-Dabbagh, A. Muhammad, R. H. Khawaja, M. Akmal, and M. Arif, “Impact of compact fluorescent lamp on power quality,” undefined, 2008. [77]Z. Wei and B. E. Hons, “Compact Fluorescent Lamps phase dependency modelling and harmonic assessment of their widespread use in distribution systems,” 2009. [78]“IEC 61000-3-2:2018 - Electromagnetic compatibility (EMC) - Part 3-2: Limits - Limits for harmonic current emissions (equipment input current ≤16 A per phase),” 1997. [Online]. Available: https://standards.iteh.ai/catalog/standards/iec/3b48ae97-3d70-455f-8ab5-40510a42744f/iec-61000-3-2-2018. [Accessed: 31-Oct-2020]. [79]J. Molina and L. Sainz, “Model of electronic ballast compact fluorescent lamps,” IEEE Trans. Power Deliv., vol. 29, no. 3, pp. 1363–1371, 2014, doi: 10.1109/TPWRD.2013.2284095. [80]“Comparativa: software de simulación electrónica | Blog personal: César Sánchez Meléndez.” [Online]. Available: https://blog.uclm.es/cesarsanchez/2017/10/29/software-de-simulacion-electronica/. [Accessed: 31-Oct-2020].
dc.rightsAtribución-CompartirIgual 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleMetodología de estimación de parámetros en dispositivos eléctricos con procesos iterativos de simulación usando algoritmos genéticos
dc.typeTesis


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