Data-driven control of interconnected energy systems

ilustraciones, gráficas, tablas

Autores:
Toro Tovar, Billy Wladimir
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/83285
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/83285
https://repositorio.unal.edu.co/
Palabra clave:
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Electric networks
Automatic control
Redes eléctricas
Control automático
Data-driven
Koopman operator
Microgrid
Distributed control
Linear predictor
Model predictive control
Operador de Koopman
Microrred
Control distribuido
Predictor lineal
Control predictivo basado en modelo
Algoritmos
algorithms
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_71add2d3eb5a836c5deb31870496c272
oai_identifier_str oai:repositorio.unal.edu.co:unal/83285
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv Data-driven control of interconnected energy systems
dc.title.translated.spa.fl_str_mv Control basado en datos para redes interconectadas de energía
title Data-driven control of interconnected energy systems
spellingShingle Data-driven control of interconnected energy systems
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Electric networks
Automatic control
Redes eléctricas
Control automático
Data-driven
Koopman operator
Microgrid
Distributed control
Linear predictor
Model predictive control
Operador de Koopman
Microrred
Control distribuido
Predictor lineal
Control predictivo basado en modelo
Algoritmos
algorithms
title_short Data-driven control of interconnected energy systems
title_full Data-driven control of interconnected energy systems
title_fullStr Data-driven control of interconnected energy systems
title_full_unstemmed Data-driven control of interconnected energy systems
title_sort Data-driven control of interconnected energy systems
dc.creator.fl_str_mv Toro Tovar, Billy Wladimir
dc.contributor.advisor.spa.fl_str_mv Mojica Nava,Eduardo Alirio
Rakoto Ravalontsalama, Naly
dc.contributor.author.spa.fl_str_mv Toro Tovar, Billy Wladimir
dc.contributor.researchgroup.spa.fl_str_mv Programa de Investigacion sobre Adquisicion y Analisis de Señales Paas-Un
LS2N, IMT-Atlantique
dc.subject.ddc.spa.fl_str_mv 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Electric networks
Automatic control
Redes eléctricas
Control automático
Data-driven
Koopman operator
Microgrid
Distributed control
Linear predictor
Model predictive control
Operador de Koopman
Microrred
Control distribuido
Predictor lineal
Control predictivo basado en modelo
Algoritmos
algorithms
dc.subject.lemb.eng.fl_str_mv Electric networks
Automatic control
dc.subject.lemb.spa.fl_str_mv Redes eléctricas
Control automático
dc.subject.proposal.eng.fl_str_mv Data-driven
Koopman operator
Microgrid
Distributed control
Linear predictor
Model predictive control
dc.subject.proposal.spa.fl_str_mv Operador de Koopman
Microrred
Control distribuido
Predictor lineal
Control predictivo basado en modelo
dc.subject.unesco.spa.fl_str_mv Algoritmos
dc.subject.unesco.eng.fl_str_mv algorithms
description ilustraciones, gráficas, tablas
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-12-14
dc.date.accessioned.none.fl_str_mv 2023-02-03T16:38:38Z
dc.date.available.none.fl_str_mv 2023-02-03T16:38:38Z
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/83285
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/83285
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Jean and D. Pitteloud, “Global Wind Power Statistics – Wind Energy International Platform,” 2019.
International Energy Agency, “Solar PV – Analysis - IEA,” 2020.
F. Milano, F. Dorfler, G. Hug, D. J. Hill, and G. Verbič, “Foundations and challenges of low-inertia systems (Invited Paper),” 20th Power Systems Computation Conference, PSCC 2018, 2018.
S. Akkari, E. Prieto-Araujo, J. Dai, O. Gomis-Bellmunt, and X. Guillaud, “Impact of the dc cable models on the svd analysis of a multi-terminal hvdc system,” in 2016 Power Systems Computation Conference (PSCC), pp. 1–6, IEEE, 2016.
M. Budišić, R. Mohr, and I. Mezić, “Applied koopmanism,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 22, no. 4, p. 047510, 2012.
Y. Susuki and A. Chakrabortty, “Introduction to Koopman Mode Decomposition for Data-Based Technology of Power System Nonlinear Dynamics,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 327–332, 2018.
V. T. Priyanga, M. Chandni, N. Mohan, and K. P. Soman, “Data-driven analysis for low frequency oscillation identification in smart grid using FB-DMD and T-DMD methods,” Proceedings of the 2019 9th International Conference on Advances in Computing and Communication, ICACC 2019, pp. 51–57, 2019.
F. Raak, Y. Susuki, and T. Hikihara, “Multi-Way Partitioning of Power Networks via Koopman Mode Analysis,” IFAC-PapersOnLine, vol. 48, no. 30, pp. 421–426, 2015.
S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Sparse identification of nonlinear dynamics with control (sindyc),” IFAC-PapersOnLine, vol. 49, no. 18, pp. 710–715, 2016.
M. Korda and I. Mezić, “Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,” Automatica, vol. 93, pp. 149–160, 2018.
M. Korda, Y. Susuki, and I. Mezić, “Power grid transient stabilization using koopman model predictive control,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 297–302, 2018.
T. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K. H. Johansson, “A survey of distributed optimization,” Annual Reviews in Control, vol. 47, no. xxxx, pp. 278–305, 2019.
R. H. Lasseter, “MicroGrids,” IEEE Power Engineering Society winter Meeting, pp. 305–308, 2002.
J. J. Justo, F. Mwasilu, J. Lee, and J. W. Jung, “AC-microgrids versus DC-microgrids with distributed energy resources: A review,” Renewable and Sustainable Energy Reviews, vol. 24, pp. 387–405, 2013.
A. Tayyebi, F. Dörfler, F. Kupzog, Z. Miletic, and W. Hribernik, “Grid-Forming Converters - Inevitability , Control Strategies and Challenges in Future Grid Applications and Challenges in Future Grids Application,” CIRED Workshop 2018, no. 0236, pp. 1–5, 2018.
S. Sen and V. Kumar, “Microgrid control: A comprehensive survey,” Annual Reviews in Control, vol. 45, no. March, pp. 118–151, 2018.
J. Schiffer, D. Zonetti, R. Ortega, A. M. Stanković, T. Sezi, and J. Raisch, “A survey on modeling of microgrids—From fundamental physics to phasors and voltage sources,” Automatica, vol. 74, pp. 135–150, 2016.
S. Moayedi and A. Davoudi, “Distributed Tertiary Control of DC Microgrid Clusters,” IEEE Transactions on Power Electronics, vol. 31, no. 2, pp. 1717–1733, 2016.
F. Dörfler, J. W. Simpson-Porco, and F. Bullo, “Breaking the hierarchy: Distributed control and economic optimality in microgrids,” IEEE Transactions on Control of Network Systems, vol. 3, no. 3, pp. 241–253, 2015.
Z. S. Hou and Z. Wang, “From model-based control to data-driven control: Survey, classification and perspective,” Information Sciences, vol. 235, pp. 3–35, 2013.
S. Yin, X. Li, H. Gao, and O. Kaynak, “Data-based techniques focused on modern industry: An overview,” IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 657–667, 2015.
Z. Hou and S. Xiong, “On model-free adaptive control and its stability analysis,” IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4555–4569, 2019.
S. L. Brunton and J. N. Kutz, Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, 2022.
F. L. Lewis and D. Vrabie, “Reinforcement learning and adaptive dynamic programming for feedback control,” IEEE Circuits and Systems Magazine, vol. 9, no. 3, pp. 32–50, 2009.
L. Buşoniu, T. de Bruin, D. Tolić, J. Kober, and I. Palunko, “Reinforcement learning for control: Performance, stability, and deep approximators,” Annual Reviews in Control, vol. 46, pp. 8–28, 2018.
B. Kiumarsi, K. G. Vamvoudakis, H. Modares, and F. L. Lewis, “Optimal and Autonomous Control Using Reinforcement Learning: A Survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2042–2062, 2018
L. Buşoniu, R. Babuška, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 38, no. 2, pp. 156–172, 2008.
M. L. Li, S. Chen, and J. Chen, “Adaptive Learning: A New Decentralized Reinforcement Learning Approach for Cooperative Multiagent Systems,” IEEE Access, vol. 8, pp. 99404–99421, 2020.
F. Dietrich, T. N. Thiem, and I. G. Kevrekidis, “On the koopman operator of algorithms,” SIAM Journal on Applied Dynamical Systems, vol. 19, no. 2, pp. 860–885, 2020.
Z. Ping, Z. Yin, X. Li, Y. Liu, and T. Yang, “Deep Koopman model predictive control for enhancing transient stability in power grids,” International Journal of Robust and Nonlinear Control, vol. 31, no. 6, pp. 1964–1978, 2021.
C. W. Rowley, I. Mezi, S. Bagheri, P. Schlatter, and D. S. Henningson, “Spectral analysis of nonlinear flows,” Journal of Fluid Mechanics, vol. 641, pp. 115–127, 2009.
M. Haseli and J. Cortés, “Approximating the Koopman operator using noisy data: Noise-resilient extended dynamic mode decomposition,” Proceedings of the American Control Conference, vol. 2019-July, pp. 5499–5504, 2019.
S. Peitz and S. Klus, “Koopman operator-based model reduction for switched-system control of PDEs,” Automatica, vol. 106, pp. 184–191, 2019.
S. Klus, F. Nüske, P. Koltai, H. Wu, I. Kevrekidis, C. Schütte, and F. Noé, “DataDriven Model Reduction and Transfer Operator Approximation,” Journal of Nonlinear Science, vol. 28, no. 3, pp. 985–1010, 2018.
J. N. Kutz, X. Fu, and S. L. Brunton, “Multiresolution dynamic mode decomposition,” SIAM Journal on Applied Dynamical Systems, vol. 15, no. 2, pp. 713–735, 2016.
A. Mauroy, Y. Susuki, and I. Mezić, “Introduction to the koopman operator in dynamical systems and control theory,” in The Koopman Operator in Systems and Control, vol. 484, pp. 3–33, Springer, 2020.
Y. Susuki and I. Mezić, “A prony approximation of Koopman Mode Decomposition,” Proceedings of the IEEE Conference on Decision and Control, vol. 54rd IEEE Conference on Decision and Control,CDC 2015, no. Cdc, pp. 7022–7027, 2015.
M. O. Williams, I. G. Kevrekidis, and C. W. Rowley, “A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition,” Journal of Nonlinear Science, vol. 25, no. 6, pp. 1307–1346, 2015.
P. J. Schmid, “Dynamic mode decomposition of numerical and experimental data,” Journal of Fluid Mechanics, vol. 656, pp. 5–28, 2010.
J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz, “On dynamic mode decomposition: Theory and applications,” Journal of Computational Dynamics, vol. 1, no. 2, pp. 391–421, 2014.
I. Mezic, “Spectral properties of dynamical systems, model reduction and decompositions,” Nonlinear Dynamics, vol. 41, pp. 309–325, 2005.
J. L. Proctor, S. L. Brunton, and J. N. Kutz, “Dynamic mode decomposition with control,” SIAM Journal on Applied Dynamical Systems, vol. 15, no. 1, pp. 142–161, 2016.
J. Mann and J. N. Kutz, “Dynamic mode decomposition for financial trading strategies,” Quantitative Finance, vol. 16, no. 11, pp. 1643–1655, 2016
N. B. Erichson, S. L. Brunton, and J. N. Kutz, “Compressed dynamic mode decomposition for background modeling,” Journal of Real-Time Image Processing, vol. 16, no. 5, pp. 1479–1492, 2019.
A. E. Saldana, E. Barocio, A. R. Messina, J. J. Ramos, R. J. Segundo, and G. A. Tinajero, “Monitoring harmonic distortion in microgrids using dynamic mode decomposition,” IEEE Power and Energy Society General Meeting, vol. 2018-Janua, pp. 1–5, 2018.
E. Kaiser, J. N. Kutz, and S. Brunton, “Data-driven discovery of Koopman eigenfunctions for control,” Machine Learning: Science and Technology, vol. 98195, pp. 1–40, 2021.
M. Korda and I. Mezic, “Optimal Construction of Koopman Eigenfunctions for Prediction and Control,” IEEE Transactions on Automatic Control, vol. 65, no. 12, pp. 5114–5129, 2020.
M. Mesbahi and M. Egerstedt, Graph theoretic methods in multiagent networks. Princeton University Press, 2010.
D. P. Bertsekas and J. N. Tsitsiklis, “Comments on "Coordination of groups of mobile autonomous agents using nearest neighbor rules",” IEEE Transactions on Automatic Control, vol. 52, no. 5, pp. 968–969, 2007.
F. L. Lewis, H. Zhang, K. Hengster-Movric, and A. Das, Cooperative Control of Multi-Agent Systems. Communications and Control Engineering, London: Springer London, 2014.
A. Mohammed, S. S. Refaat, S. Bayhan, and H. Abu-Rub, “Ac microgrid control and management strategies: Evaluation and review,” IEEE Power Electronics Magazine, vol. 6, no. 2, pp. 18–31, 2019.
D. K. Molzahn, F. Dörfler, H. Sandberg, S. H. Low, S. Chakrabarti, R. Baldick, and J. Lavaei, “A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2941–2962, 2017.
R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, 2007.
Dörfler, Florian, “Distributed consensus-based optimization review of optimization theory.” https://people.ee.ethz.ch/~floriand/docs/Teaching/ATIC_ 2017/Optimization_Lecture.pdf, 2017. Accessed: 2021-02-06.
T. Tatarenko and B. Touri, “Non-Convex Distributed Optimization,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 3744–3757, 2017.
W. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality: Second Edition. United States: Wiley-Blackwell, Sept. 2011.
M. Korda, Y. Susuki, and I. Mezić, “Power grid transient stabilization using Koopman model predictive control,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 297–302, 2018.
T. Goldstein, B. O’Donoghue, S. Setzep, and R. Baraniuk, “Fast alternating direction optimization methods,” SIAM Journal on Imaging Sciences, vol. 7, no. 3, pp. 1588– 1623, 2014.
H. Zhang, C. W. Rowley, E. A. Deem, and L. N. Cattafesta, “Online dynamic mode decomposition for time-varying systems,” SIAM Journal on Applied Dynamical Systems, vol. 18, no. 3, pp. 1586–1609, 2019.
M. Wanner and I. Mezić, “Robust approximation of the stochastic koopman operator,” SIAM Journal on Applied Dynamical Systems, vol. 21, no. 3, pp. 1930–1951, 2022.
S. Sinha, B. Huang, and U. Vaidya, “On Robust Computation of Koopman Operator and Prediction in Random Dynamical Systems,” Journal of Nonlinear Science, vol. 30, no. 5, pp. 2057–2090, 2020.
P. Tielens and D. Van Hertem, “The relevance of inertia in power systems,” Renewable and Sustainable Energy Reviews, vol. 55, no. 2016, pp. 999–1009, 2016.
N. Pogaku, M. Prodanović, and T. C. Green, “Modeling, analysis and testing of autonomous operation of an inverter-based microgrid,” IEEE Transactions on Power Electronics, vol. 22, no. 2, pp. 613–625, 2007.
J. Rocabert, A. Luna, F. Blaabjerg, and P. Rodríguez, “Control of power converters in ac microgrids,” IEEE Transactions on Power Electronics, vol. 27, no. 11, pp. 4734– 4749, 2012.
S. Anderson, P. Hidalgo-Gonzalez, R. Dobbe, and C. J. Tomlin, “Distributed Model Predictive Control for Autonomous Droop-Controlled Inverter-Based Microgrids,” Proceedings of the IEEE Conference on Decision and Control, vol. 2019-Decem, no. Cdc, pp. 6242–6248, 2019.
Q.-C. Zhong and Y. Zeng, “Universal droop control of inverters with different types of output impedance,” IEEE access, vol. 4, pp. 702–712, 2016.
Q. Liu, Y. Tao, X. Liu, Y. Deng, and X. He, “Voltage unbalance and harmonics compensation for islanded microgrid inverters,” IET Power Electronics, vol. 7, no. 5, pp. 1055–1063, 2014.
J. Schiffer, R. Ortega, A. Astolfi, J. Raisch, and T. Sezi, “Conditions for stability of droop-controlled inverter-based microgrids,” Automatica, vol. 50, no. 10, pp. 2457– 2469, 2014.
J. M. Guerrero, J. C. Vasquez, J. Matas, L. G. De Vicuña, and M. Castilla, “Hierarchical control of droop-controlled AC and DC microgrids - A general approach toward standardization,” IEEE Transactions on Industrial Electronics, vol. 58, no. 1, pp. 158–172, 2011.
J. M. Guerrero, M. Chandorkar, T. Lee, and P. C. Loh, “Advanced Control Architectures for Intelligent Microgrids; Part I: Decentralized and Hierarchical Control,” Industrial Electronics, IEEE Transactions on, vol. 60, no. 4, pp. 1254–1262, 2013.
J. Hu, Y. Shan, J. M. Guerrero, A. Ioinovici, K. W. Chan, and J. Rodriguez, “Model predictive control of microgrids – An overview,” Renewable and Sustainable Energy Reviews, vol. 136, no. September 2020, p. 110422, 2021.
R. P. Aguilera and D. E. Quevedo, “Predictive control of power converters: Designs with guaranteed performance,” IEEE Transactions on Industrial Informatics, vol. 11, no. 1, pp. 53–63, 2015.
G. Qu and N. Li, “Optimal Distributed Feedback Voltage Control under Limited Reactive Power,” IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 315–331, 2020.
L. M. Leon, A. S. Bretas, and S. Rivera, “Quadratically constrained quadratic programming formulation of contingency constrained optimal power flow with photovoltaic generation,” Energies, vol. 13, no. 13, 2020.
J. Boudreaux, “Design, Simulation, and Construction of an IEEE 14-Bus Power System,” LSU Master’s Theses, sep 2018.
J. Lofberg, “Yalmip : a toolbox for modeling and optimization in matlab,” in 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508), pp. 284–289, 2004.
P. D. Christofides, R. Scattolini, D. Muñoz de la Peña, and J. Liu, “Distributed model predictive control: A tutorial review and future research directions,” Computers and Chemical Engineering, vol. 51, pp. 21–41, 2013.
J. Maestre and R. Negenborn, Distributed Model Predictive Control Made Easy, vol. 69 of Intelligent Systems, Control and Automation: Science and Engineering; Distributed Model Predictive Control Made Easy. Springer Netherlands, 2014.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1–122, 2010.
P. Šulc, S. Backhaus, and M. Chertkov, “Optimal distributed control of reactive power via the alternating direction method of multipliers,” IEEE Transactions on Energy Conversion, vol. 29, no. 4, pp. 968–977, 2014.
F. L. Lewis, D. Vrabie, and V. L. Syrmos, Optimal control. John Wiley & Sons, 2012.
C. Conte, C. N. Jones, M. Morari, and M. N. Zeilinger, “Distributed synthesis and stability of cooperative distributed model predictive control for linear systems,” Automatica, vol. 69, pp. 117–125, 2016.
M. Chen, J. Zhao, Z. Xu, Y. Liu, Y. Zhu, and Z. Shao, “Cooperative distributed model predictive control based on topological hierarchy decomposition,” Control Engineering Practice, vol. 103, no. August, 2020.
D. Fu, H.-T. Zhang, A. Dutta, and G. Chen, “A cooperative distributed model predictive control approach to supply chain management,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 12, pp. 4894–4904, 2020.
S. P. Nandanoori, S. Pal, S. Sinha, S. Kundu, K. Agarwal, and S. Choudhury, “Data-driven Distributed Learning of Multi-agent Systems: A Koopman Operator Approach,” Proceedings of the IEEE Conference on Decision and Control, vol. 2021- December, no. Cdc, pp. 5059–5066, 2021.
B. T. Stewart, A. N. Venkat, J. B. Rawlings, S. J. Wright, and G. Pannocchia, “Cooperative distributed model predictive control,” Systems & Control Letters, vol. 59, no. 8, pp. 460–469, 2010.
N. Bastianello, A. Simonetto, and R. Carli, “Distributed prediction-correction admm for time-varying convex optimization,” in 2020 54th Asilomar Conference on Signals, Systems, and Computers, pp. 47–52, 2020.
E. Wei and A. Ozdaglar, “Distributed alternating direction method of multipliers,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pp. 5445–5450, 2012.
H. Arbabi and I. Mezić, “Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator,” SIAM Journal on Applied Dynamical Systems, vol. 16, no. 4, pp. 2096–2126, 2017.
W. W. Hager, “Updating the inverse of a matrix,” SIAM Review, vol. 31, no. 2, pp. 221–239, 1989.
X. Zhang, W. Pan, R. Scattolini, S. Yu, and X. Xu, “Robust tube-based model predictive control with Koopman operators,” Automatica, vol. 137, p. 110114, 2022.
M. Akinkunmi, Introduction to Statistical Analysis, pp. 1–13. Cham: Springer International Publishing, 2019.
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 123 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Eléctrica
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/83285/1/license.txt
https://repositorio.unal.edu.co/bitstream/unal/83285/2/80251045.2022.pdf
bitstream.checksum.fl_str_mv eb34b1cf90b7e1103fc9dfd26be24b4a
411e258559fd1d43ef9838bb03dd19e6
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1806886331830239232
spelling Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mojica Nava,Eduardo Alirioe4a1a8ad2ab3b2c45a8785177a841de1600Rakoto Ravalontsalama, Nalyf19bdaf8cf0222a41ca56e9b5cd4aa14600Toro Tovar, Billy Wladimirae8d6f37a58cc33169dd8b2e31cd9dabPrograma de Investigacion sobre Adquisicion y Analisis de Señales Paas-UnLS2N, IMT-Atlantique2023-02-03T16:38:38Z2023-02-03T16:38:38Z2022-12-14https://repositorio.unal.edu.co/handle/unal/83285Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasThis research proposed several algorithms for the identification and control of microgrids based on the Koopman operator. The contributions presented in this manuscript are focused on the control of voltage and reactive power. We have considered five control scenarios based on the Koopman operator: (i) a centralized algorithm that regulates the microgrid voltage without sharing information using MPC. (ii) a non-cooperative distributed control, with a consensus term in the restrictions, that regulates the voltage based on the Koopman model of the inverters. (iii) a cooperative distributed MPC that uses the microgrid Koopman model, where the agents share their control inputs to generate the control signals. Here, we identify the input matrices by using data. (iv) a distributed control that uses data to identify the system error to design an ADMM algorithm. (v) an online data-driven controller that regulates the microgrid voltage and an analysis of the eigenvalues of the system and the effects of noisy measurements.Esta investigación propone varios algoritmos para la identificación y el control de microrredes eléctricas basados en el operador de Koopman. Las contribuciones que presentamos en este manuscrito se enfocan en el control de voltaje y de la potencia reactiva. Hemos considerado cinco escenarios de control basados en el operador de Koopman: (i) Un algoritmo centralizado que regula el voltaje de la microrred sin necesidad de compartir información y que usa MPC. (ii) un control distribuido no cooperativo, con un término de consenso en las restricciones del problema de optimización, que regula el voltaje y que se basa en el modelo de los inversores en el espacio de Koopman (iii) un control distribuido cooperativo que usa el modelo de la microrred en el espacio de Koopman, en donde los agentes usan las señales de control tomadas por otros agentes para generar sus propias señales. Aquí, identificamos las matrices de entrada usando datos. (iv) Un control distribuido, que usa datos para identificar el error del sistema, para diseñar un algoritmo basado en ADMM. (v) un controlador en línea basado en datos que regula el voltaje de la microrred. También, un análisis de los valores propios del sistema y los efectos de mediciones con ruido. (Texto tomado de la fuente).DoctoradoDoctor en Ingeniería123 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería EléctricaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaElectric networksAutomatic controlRedes eléctricasControl automáticoData-drivenKoopman operatorMicrogridDistributed controlLinear predictorModel predictive controlOperador de KoopmanMicrorredControl distribuidoPredictor linealControl predictivo basado en modeloAlgoritmosalgorithmsData-driven control of interconnected energy systemsControl basado en datos para redes interconectadas de energíaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDJean and D. Pitteloud, “Global Wind Power Statistics – Wind Energy International Platform,” 2019.International Energy Agency, “Solar PV – Analysis - IEA,” 2020.F. Milano, F. Dorfler, G. Hug, D. J. Hill, and G. Verbič, “Foundations and challenges of low-inertia systems (Invited Paper),” 20th Power Systems Computation Conference, PSCC 2018, 2018.S. Akkari, E. Prieto-Araujo, J. Dai, O. Gomis-Bellmunt, and X. Guillaud, “Impact of the dc cable models on the svd analysis of a multi-terminal hvdc system,” in 2016 Power Systems Computation Conference (PSCC), pp. 1–6, IEEE, 2016.M. Budišić, R. Mohr, and I. Mezić, “Applied koopmanism,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 22, no. 4, p. 047510, 2012.Y. Susuki and A. Chakrabortty, “Introduction to Koopman Mode Decomposition for Data-Based Technology of Power System Nonlinear Dynamics,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 327–332, 2018.V. T. Priyanga, M. Chandni, N. Mohan, and K. P. Soman, “Data-driven analysis for low frequency oscillation identification in smart grid using FB-DMD and T-DMD methods,” Proceedings of the 2019 9th International Conference on Advances in Computing and Communication, ICACC 2019, pp. 51–57, 2019.F. Raak, Y. Susuki, and T. Hikihara, “Multi-Way Partitioning of Power Networks via Koopman Mode Analysis,” IFAC-PapersOnLine, vol. 48, no. 30, pp. 421–426, 2015.S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Sparse identification of nonlinear dynamics with control (sindyc),” IFAC-PapersOnLine, vol. 49, no. 18, pp. 710–715, 2016.M. Korda and I. Mezić, “Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,” Automatica, vol. 93, pp. 149–160, 2018.M. Korda, Y. Susuki, and I. Mezić, “Power grid transient stabilization using koopman model predictive control,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 297–302, 2018.T. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K. H. Johansson, “A survey of distributed optimization,” Annual Reviews in Control, vol. 47, no. xxxx, pp. 278–305, 2019.R. H. Lasseter, “MicroGrids,” IEEE Power Engineering Society winter Meeting, pp. 305–308, 2002.J. J. Justo, F. Mwasilu, J. Lee, and J. W. Jung, “AC-microgrids versus DC-microgrids with distributed energy resources: A review,” Renewable and Sustainable Energy Reviews, vol. 24, pp. 387–405, 2013.A. Tayyebi, F. Dörfler, F. Kupzog, Z. Miletic, and W. Hribernik, “Grid-Forming Converters - Inevitability , Control Strategies and Challenges in Future Grid Applications and Challenges in Future Grids Application,” CIRED Workshop 2018, no. 0236, pp. 1–5, 2018.S. Sen and V. Kumar, “Microgrid control: A comprehensive survey,” Annual Reviews in Control, vol. 45, no. March, pp. 118–151, 2018.J. Schiffer, D. Zonetti, R. Ortega, A. M. Stanković, T. Sezi, and J. Raisch, “A survey on modeling of microgrids—From fundamental physics to phasors and voltage sources,” Automatica, vol. 74, pp. 135–150, 2016.S. Moayedi and A. Davoudi, “Distributed Tertiary Control of DC Microgrid Clusters,” IEEE Transactions on Power Electronics, vol. 31, no. 2, pp. 1717–1733, 2016.F. Dörfler, J. W. Simpson-Porco, and F. Bullo, “Breaking the hierarchy: Distributed control and economic optimality in microgrids,” IEEE Transactions on Control of Network Systems, vol. 3, no. 3, pp. 241–253, 2015.Z. S. Hou and Z. Wang, “From model-based control to data-driven control: Survey, classification and perspective,” Information Sciences, vol. 235, pp. 3–35, 2013.S. Yin, X. Li, H. Gao, and O. Kaynak, “Data-based techniques focused on modern industry: An overview,” IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 657–667, 2015.Z. Hou and S. Xiong, “On model-free adaptive control and its stability analysis,” IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4555–4569, 2019.S. L. Brunton and J. N. Kutz, Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, 2022.F. L. Lewis and D. Vrabie, “Reinforcement learning and adaptive dynamic programming for feedback control,” IEEE Circuits and Systems Magazine, vol. 9, no. 3, pp. 32–50, 2009.L. Buşoniu, T. de Bruin, D. Tolić, J. Kober, and I. Palunko, “Reinforcement learning for control: Performance, stability, and deep approximators,” Annual Reviews in Control, vol. 46, pp. 8–28, 2018.B. Kiumarsi, K. G. Vamvoudakis, H. Modares, and F. L. Lewis, “Optimal and Autonomous Control Using Reinforcement Learning: A Survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2042–2062, 2018L. Buşoniu, R. Babuška, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 38, no. 2, pp. 156–172, 2008.M. L. Li, S. Chen, and J. Chen, “Adaptive Learning: A New Decentralized Reinforcement Learning Approach for Cooperative Multiagent Systems,” IEEE Access, vol. 8, pp. 99404–99421, 2020.F. Dietrich, T. N. Thiem, and I. G. Kevrekidis, “On the koopman operator of algorithms,” SIAM Journal on Applied Dynamical Systems, vol. 19, no. 2, pp. 860–885, 2020.Z. Ping, Z. Yin, X. Li, Y. Liu, and T. Yang, “Deep Koopman model predictive control for enhancing transient stability in power grids,” International Journal of Robust and Nonlinear Control, vol. 31, no. 6, pp. 1964–1978, 2021.C. W. Rowley, I. Mezi, S. Bagheri, P. Schlatter, and D. S. Henningson, “Spectral analysis of nonlinear flows,” Journal of Fluid Mechanics, vol. 641, pp. 115–127, 2009.M. Haseli and J. Cortés, “Approximating the Koopman operator using noisy data: Noise-resilient extended dynamic mode decomposition,” Proceedings of the American Control Conference, vol. 2019-July, pp. 5499–5504, 2019.S. Peitz and S. Klus, “Koopman operator-based model reduction for switched-system control of PDEs,” Automatica, vol. 106, pp. 184–191, 2019.S. Klus, F. Nüske, P. Koltai, H. Wu, I. Kevrekidis, C. Schütte, and F. Noé, “DataDriven Model Reduction and Transfer Operator Approximation,” Journal of Nonlinear Science, vol. 28, no. 3, pp. 985–1010, 2018.J. N. Kutz, X. Fu, and S. L. Brunton, “Multiresolution dynamic mode decomposition,” SIAM Journal on Applied Dynamical Systems, vol. 15, no. 2, pp. 713–735, 2016.A. Mauroy, Y. Susuki, and I. Mezić, “Introduction to the koopman operator in dynamical systems and control theory,” in The Koopman Operator in Systems and Control, vol. 484, pp. 3–33, Springer, 2020.Y. Susuki and I. Mezić, “A prony approximation of Koopman Mode Decomposition,” Proceedings of the IEEE Conference on Decision and Control, vol. 54rd IEEE Conference on Decision and Control,CDC 2015, no. Cdc, pp. 7022–7027, 2015.M. O. Williams, I. G. Kevrekidis, and C. W. Rowley, “A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition,” Journal of Nonlinear Science, vol. 25, no. 6, pp. 1307–1346, 2015.P. J. Schmid, “Dynamic mode decomposition of numerical and experimental data,” Journal of Fluid Mechanics, vol. 656, pp. 5–28, 2010.J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz, “On dynamic mode decomposition: Theory and applications,” Journal of Computational Dynamics, vol. 1, no. 2, pp. 391–421, 2014.I. Mezic, “Spectral properties of dynamical systems, model reduction and decompositions,” Nonlinear Dynamics, vol. 41, pp. 309–325, 2005.J. L. Proctor, S. L. Brunton, and J. N. Kutz, “Dynamic mode decomposition with control,” SIAM Journal on Applied Dynamical Systems, vol. 15, no. 1, pp. 142–161, 2016.J. Mann and J. N. Kutz, “Dynamic mode decomposition for financial trading strategies,” Quantitative Finance, vol. 16, no. 11, pp. 1643–1655, 2016N. B. Erichson, S. L. Brunton, and J. N. Kutz, “Compressed dynamic mode decomposition for background modeling,” Journal of Real-Time Image Processing, vol. 16, no. 5, pp. 1479–1492, 2019.A. E. Saldana, E. Barocio, A. R. Messina, J. J. Ramos, R. J. Segundo, and G. A. Tinajero, “Monitoring harmonic distortion in microgrids using dynamic mode decomposition,” IEEE Power and Energy Society General Meeting, vol. 2018-Janua, pp. 1–5, 2018.E. Kaiser, J. N. Kutz, and S. Brunton, “Data-driven discovery of Koopman eigenfunctions for control,” Machine Learning: Science and Technology, vol. 98195, pp. 1–40, 2021.M. Korda and I. Mezic, “Optimal Construction of Koopman Eigenfunctions for Prediction and Control,” IEEE Transactions on Automatic Control, vol. 65, no. 12, pp. 5114–5129, 2020.M. Mesbahi and M. Egerstedt, Graph theoretic methods in multiagent networks. Princeton University Press, 2010.D. P. Bertsekas and J. N. Tsitsiklis, “Comments on "Coordination of groups of mobile autonomous agents using nearest neighbor rules",” IEEE Transactions on Automatic Control, vol. 52, no. 5, pp. 968–969, 2007.F. L. Lewis, H. Zhang, K. Hengster-Movric, and A. Das, Cooperative Control of Multi-Agent Systems. Communications and Control Engineering, London: Springer London, 2014.A. Mohammed, S. S. Refaat, S. Bayhan, and H. Abu-Rub, “Ac microgrid control and management strategies: Evaluation and review,” IEEE Power Electronics Magazine, vol. 6, no. 2, pp. 18–31, 2019.D. K. Molzahn, F. Dörfler, H. Sandberg, S. H. Low, S. Chakrabarti, R. Baldick, and J. Lavaei, “A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2941–2962, 2017.R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, 2007.Dörfler, Florian, “Distributed consensus-based optimization review of optimization theory.” https://people.ee.ethz.ch/~floriand/docs/Teaching/ATIC_ 2017/Optimization_Lecture.pdf, 2017. Accessed: 2021-02-06.T. Tatarenko and B. Touri, “Non-Convex Distributed Optimization,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 3744–3757, 2017.W. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality: Second Edition. United States: Wiley-Blackwell, Sept. 2011.M. Korda, Y. Susuki, and I. Mezić, “Power grid transient stabilization using Koopman model predictive control,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 297–302, 2018.T. Goldstein, B. O’Donoghue, S. Setzep, and R. Baraniuk, “Fast alternating direction optimization methods,” SIAM Journal on Imaging Sciences, vol. 7, no. 3, pp. 1588– 1623, 2014.H. Zhang, C. W. Rowley, E. A. Deem, and L. N. Cattafesta, “Online dynamic mode decomposition for time-varying systems,” SIAM Journal on Applied Dynamical Systems, vol. 18, no. 3, pp. 1586–1609, 2019.M. Wanner and I. Mezić, “Robust approximation of the stochastic koopman operator,” SIAM Journal on Applied Dynamical Systems, vol. 21, no. 3, pp. 1930–1951, 2022.S. Sinha, B. Huang, and U. Vaidya, “On Robust Computation of Koopman Operator and Prediction in Random Dynamical Systems,” Journal of Nonlinear Science, vol. 30, no. 5, pp. 2057–2090, 2020.P. Tielens and D. Van Hertem, “The relevance of inertia in power systems,” Renewable and Sustainable Energy Reviews, vol. 55, no. 2016, pp. 999–1009, 2016.N. Pogaku, M. Prodanović, and T. C. Green, “Modeling, analysis and testing of autonomous operation of an inverter-based microgrid,” IEEE Transactions on Power Electronics, vol. 22, no. 2, pp. 613–625, 2007.J. Rocabert, A. Luna, F. Blaabjerg, and P. Rodríguez, “Control of power converters in ac microgrids,” IEEE Transactions on Power Electronics, vol. 27, no. 11, pp. 4734– 4749, 2012.S. Anderson, P. Hidalgo-Gonzalez, R. Dobbe, and C. J. Tomlin, “Distributed Model Predictive Control for Autonomous Droop-Controlled Inverter-Based Microgrids,” Proceedings of the IEEE Conference on Decision and Control, vol. 2019-Decem, no. Cdc, pp. 6242–6248, 2019.Q.-C. Zhong and Y. Zeng, “Universal droop control of inverters with different types of output impedance,” IEEE access, vol. 4, pp. 702–712, 2016.Q. Liu, Y. Tao, X. Liu, Y. Deng, and X. He, “Voltage unbalance and harmonics compensation for islanded microgrid inverters,” IET Power Electronics, vol. 7, no. 5, pp. 1055–1063, 2014.J. Schiffer, R. Ortega, A. Astolfi, J. Raisch, and T. Sezi, “Conditions for stability of droop-controlled inverter-based microgrids,” Automatica, vol. 50, no. 10, pp. 2457– 2469, 2014.J. M. Guerrero, J. C. Vasquez, J. Matas, L. G. De Vicuña, and M. Castilla, “Hierarchical control of droop-controlled AC and DC microgrids - A general approach toward standardization,” IEEE Transactions on Industrial Electronics, vol. 58, no. 1, pp. 158–172, 2011.J. M. Guerrero, M. Chandorkar, T. Lee, and P. C. Loh, “Advanced Control Architectures for Intelligent Microgrids; Part I: Decentralized and Hierarchical Control,” Industrial Electronics, IEEE Transactions on, vol. 60, no. 4, pp. 1254–1262, 2013.J. Hu, Y. Shan, J. M. Guerrero, A. Ioinovici, K. W. Chan, and J. Rodriguez, “Model predictive control of microgrids – An overview,” Renewable and Sustainable Energy Reviews, vol. 136, no. September 2020, p. 110422, 2021.R. P. Aguilera and D. E. Quevedo, “Predictive control of power converters: Designs with guaranteed performance,” IEEE Transactions on Industrial Informatics, vol. 11, no. 1, pp. 53–63, 2015.G. Qu and N. Li, “Optimal Distributed Feedback Voltage Control under Limited Reactive Power,” IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 315–331, 2020.L. M. Leon, A. S. Bretas, and S. Rivera, “Quadratically constrained quadratic programming formulation of contingency constrained optimal power flow with photovoltaic generation,” Energies, vol. 13, no. 13, 2020.J. Boudreaux, “Design, Simulation, and Construction of an IEEE 14-Bus Power System,” LSU Master’s Theses, sep 2018.J. Lofberg, “Yalmip : a toolbox for modeling and optimization in matlab,” in 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508), pp. 284–289, 2004.P. D. Christofides, R. Scattolini, D. Muñoz de la Peña, and J. Liu, “Distributed model predictive control: A tutorial review and future research directions,” Computers and Chemical Engineering, vol. 51, pp. 21–41, 2013.J. Maestre and R. Negenborn, Distributed Model Predictive Control Made Easy, vol. 69 of Intelligent Systems, Control and Automation: Science and Engineering; Distributed Model Predictive Control Made Easy. Springer Netherlands, 2014.S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1–122, 2010.P. Šulc, S. Backhaus, and M. Chertkov, “Optimal distributed control of reactive power via the alternating direction method of multipliers,” IEEE Transactions on Energy Conversion, vol. 29, no. 4, pp. 968–977, 2014.F. L. Lewis, D. Vrabie, and V. L. Syrmos, Optimal control. John Wiley & Sons, 2012.C. Conte, C. N. Jones, M. Morari, and M. N. Zeilinger, “Distributed synthesis and stability of cooperative distributed model predictive control for linear systems,” Automatica, vol. 69, pp. 117–125, 2016.M. Chen, J. Zhao, Z. Xu, Y. Liu, Y. Zhu, and Z. Shao, “Cooperative distributed model predictive control based on topological hierarchy decomposition,” Control Engineering Practice, vol. 103, no. August, 2020.D. Fu, H.-T. Zhang, A. Dutta, and G. Chen, “A cooperative distributed model predictive control approach to supply chain management,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 12, pp. 4894–4904, 2020.S. P. Nandanoori, S. Pal, S. Sinha, S. Kundu, K. Agarwal, and S. Choudhury, “Data-driven Distributed Learning of Multi-agent Systems: A Koopman Operator Approach,” Proceedings of the IEEE Conference on Decision and Control, vol. 2021- December, no. Cdc, pp. 5059–5066, 2021.B. T. Stewart, A. N. Venkat, J. B. Rawlings, S. J. Wright, and G. Pannocchia, “Cooperative distributed model predictive control,” Systems & Control Letters, vol. 59, no. 8, pp. 460–469, 2010.N. Bastianello, A. Simonetto, and R. Carli, “Distributed prediction-correction admm for time-varying convex optimization,” in 2020 54th Asilomar Conference on Signals, Systems, and Computers, pp. 47–52, 2020.E. Wei and A. Ozdaglar, “Distributed alternating direction method of multipliers,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pp. 5445–5450, 2012.H. Arbabi and I. Mezić, “Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator,” SIAM Journal on Applied Dynamical Systems, vol. 16, no. 4, pp. 2096–2126, 2017.W. W. Hager, “Updating the inverse of a matrix,” SIAM Review, vol. 31, no. 2, pp. 221–239, 1989.X. Zhang, W. Pan, R. Scattolini, S. Yu, and X. Xu, “Robust tube-based model predictive control with Koopman operators,” Automatica, vol. 137, p. 110114, 2022.M. Akinkunmi, Introduction to Statistical Analysis, pp. 1–13. Cham: Springer International Publishing, 2019.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83285/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL80251045.2022.pdf80251045.2022.pdfTesis de Doctorado en Ingeniería - Ingeniería Eléctricaapplication/pdf7065053https://repositorio.unal.edu.co/bitstream/unal/83285/2/80251045.2022.pdf411e258559fd1d43ef9838bb03dd19e6MD52unal/83285oai:repositorio.unal.edu.co:unal/832852023-02-03 11:41:20.364Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Publicaciones similares