Tetiana Bogodorova

Associate Professor at APPS UCU.

Teaches such courses as:

  1. Reinforcement Learning
  2. Introduction to Robotics
  3. Principles of Data Processing (Bachelor Level course)
  4. Machine Learning (Master Level course)
  5. Academic Writing (Co-lecturer)

E-mail:

LinkedIn page

https://www.linkedin.com/in/tetiana-bogodorova-ph-d-20711763/

Education:

Ph.D. in Electrical Engineering, December 2017

KTH Royal Institute of Technology, Stockholm, Sweden

Dissertation: Modeling, Model Validation and Uncertainty Identification for Power System Analysis

Advisors: Dr. Luigi Vanfretti, Dr. Konstantin Turitsyn

 

M.Sc. in Automatic and Control Systems, June 2009

National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv,

Ukraine. Thesis: “Multivariate Numerical Control System Synthesis”

Advisor: Dr. Natalia Repnikova

 

B.Sc. in Computerized Systems, Automatics and Control, July 2007

National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv,

Ukraine.

Thesis: “Methods of accuracy improvements of automatic control systems”

Advisor: Tetiana Lukina

 

Work experience:

Associate Professor at UCU, Lviv, Ukraine (09.2018 – present)

Research Associate at UCU, Lviv, Ukraine (02.2018-09.2018)

Data Scientist at ABM Cloud, Kyiv, Ukraine (04.2018- 12.2018)

PhD student at KTH Royal Institute of Technology, Stockholm, Sweden (09.2012 – 12.2017)

Visiting PhD student at Massachusetts Institute of Technology, Cambridge, MA, USA (02.2014-05.2014)

System/Business Analyst at NetCracker, Kyiv, Ukraine (10.2007-12.2011)

Tetiana’s scientific research is data-driven modeling considering and quantifying uncertainties. It focuses on developing methods for power system modeling and model validation. This research is multidisciplinary since it requires to involve knowledge from such fields as signal processing, control theory, statistics, and computer science machine learning to solve power system problems.

Current areas of research:

  • Modeling of distributed energy resources in power grid
  • Machine learning for power grid problems
  • Optimal load control in power grid
  • Modeling and prescriptive analytics of other cyber-physical systems

 

Journal papers

[1] M. Baudette, M. Castro, T. Rabuzin, J. Lavenius, T. Bogodorova, and L. Vanfretti. “OpenIPSL: Open-Instance Power System Library—Update 1.5 to iTesla Power Systems Library (iPSL): A Modelica library for phasor time-domain simulations”. In: SoftwareX 7 (2018), pp. 34–36.

[2] T. Bogodorova and L. Vanfretti. “Model Structure Choice for a Static Var Compensator under Modeling Uncertainty and Incomplete Information”. In: IEEE Access (Oct. 2017). ISSN: 2169-3536. DOI: 10.1109/ACCESS.2017.2758845.

[3] T. Bogodorova, L. Vanfretti, V. S. Peric, and K. Turitsyn. “Identifying Uncertainty Distributions and Confidence Regions of Power Plant Parameters”. In: IEEE Access 5 (Sept. 2017), pp. 19213–19224. ISSN: 2169-3536. DOI: 10.1109/ACCESS.2017.2754346.

[4] L. Vanfretti, M. Baudette, A. Amazouz, T. Bogodorova, T. Rabuzin, J. Lavenius, and F. J. Gomez-Lopez. “RaPiD: A modular and extensible toolbox for parameter estimation of Modelica and FMI compliant models”. In: SoftwareX 5 (2016), pp. 144–149. URL: ttp://dx.doi.org/10.1016/j.softx.2016.07.004.

Conference papers

[1] M. Aguilera, L. Vanfretti, F. Gomez, and T. Bogodorova. “Coalesced Gas Turbine and Power System Modeling and Simulation using Modelica”. In: The American Modelica Conference 2018, Oct. 2018.

[2] N. Johannesson, T. Bogodorova, and L. Vanfretti. “Identifying Low-Order Frequency-Dependent Transmission Line Model Parameters”. In: IEEE PES Innovative Smart Grid Technologies (ISGT Europe), Sept. 2017.

[3] T. Bogodorova, L. Vanfretti, and K. Turitsyn. “Voltage control-based ancillary service using thermostatically controlled loads”. In: 2016 IEEE Power and Energy Society General Meeting (PESGM). July 2016, pp. 1–5. DOI10.1109/PESGM.2016.7741640.

[4] V. S. Peric, T. Bogodorova, A. N. Mete, and L. Vanfretti. “Model order selection for probing-based power system mode estimation”. In: 2015 IEEE Power and Energy Conference at Illinois (PECI). 2015, pp. 1–5.

[5] T. Bogodorova, L. Vanfretti, and K. Turitsyn. “Bayesian Parameter Estimation of Power System Primary Frequency Controls under Modeling Uncertainties”. In: IFAC-PapersOnLine. Vol. 48. 28. Elsevier, 2015, pp. 461–465.

[6] L. Vanfretti, T. Bogodorova, and M. Baudette. “A Modelica power system component library for model validation and parameter identification”. In: Proceedings of the 10 th International Modelica Conference, Lund, Sweden, 10-12 March. 096. Linkoping University Electronic Press. 2014, pp. 1195–1203.

[7] L. Vanfretti, T. Bogodorova, and M. Baudette. “Power system model identification exploiting the Modelica language and FMI technologies”. In: 2014 IEEE International Conference on Intelligent Energy and Power Systems (IEPS). Kyiv, June 2014, pp. 127–132. DOI: 10.1109/IEPS.2014.6874164.

[8] L. Vanfretti, W. Li, T. Bogodorova, and P. Panciatici. “Unambiguous Power System Dynamic Modeling and Simulation using Modelica Tools”. In: 2013 IEEE Power and Energy Society General Meeting (PES). IEEE. 2013, pp. 1–5.

[9] T. Bogodorova, M. Sabate, G. Leon, L. Vanfretti, M. Halat, J. Heyberger, and P. Panciatici. “A Modelica Power System Library for Phasor Timedomain Simulation”. In: 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). IEEE. 2013, pp. 1–5.

[10] L. Vanfretti, W. Li, and T. Bogodorova. “Unambiguous Power System Dynamic Modeling and Simulation using Modelica Tools”. In: 7th MODPROD Workshop on Model-Based Product Development. 2013.

Technical reports

[1] S. Cole, T. Bogodorova, L. Vanfretti, G. Leon, and M. Sebate. Deliverable D3.1 Part II: Limitations of current modelling approaches. Tech. rep. iTesla EU Project, 2012. URL: http://www.itesla – project.eu /deliverables.html.Accessed:2017-08-10.

[2] T. Bogodorova, L. Vanfretti, J.-B. Heyberger, et al. Deliverable D3.1 Requirements for validation of Phasor Time domain simulations. Tech. rep. iTesla EU Project, 2012. URL: http :// www . itesla – project . eu/deliverables.html. Accessed:2017-08-10.