Selected journal papers

• T. Bogodorova, D. Osipov, and L. Vanfretti. “Fast small signal stability assessment using deep convolutional neural networks”. In: Electric Power Systems Research 235 (2024), p. 110853. DOI: 10.1016/j.epsr.2024.110853. [Ref.]

• F. Fachini, T. Bogodorova, L. Vanfretti, and S. Boersma. “A microgrid control scheme for islanded operation and re-synchronization utilizing Model Predictive Control”. In: Sustainable Energy, Grids and Networks (2024), p. 101464. DOI: 10.1016/j.segan.2024.101464. [Ref.]

• T. Bogodorova, D. Osipov, and Joe H. Chow. “Misclassification Prediction for Transient Stability Assessment”. In: Under review (2024).

• S. A. Dorado-Rojas, F. Fachini, T. Bogodorova, G. Laera, M. De Castro Fernandes, and L. Vanfretti. “ModelicaGridData: Massive power system simulation data generation and labeling tool using Modelica and Python”. In: SoftwareX 21 (2023), p. 101258. DOI: 10.1016/j.softx.2022.101258. [Ref.]

• O. Lukianykhin, T. Bogodorova. “Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning”. In: Energies 14.8 (2021), p. 2274. DOI: 10.3390/en14082274. [Ref.]

• T. Bogodorova, D. Osipov, and L. Vanfretti. “Automated Design of Realistic Contingencies for Big Data Generation”. In: IEEE Transactions on Power Systems (2020). DOI: 10.1109/TPWRS.2020.3020726. [Ref.]

• O. Lukianykhin, T. Bogodorova. "ModelicaGym: applying reinforcement learning to Modelica models." Proceedings of the 9th International Workshop on Equation-based Object-oriented Modeling Languages and Tools. 2019. [Ref.]

• T. Bogodorova, 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. [Ref.]

• 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. [Ref.]

• 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: http://dx.doi.org/10.1016/j.softx.2016.07.004. [Ref.]

 

Recent conference papers

1. T. Bogodorova, D. Osipov, and L. Vanfretti. “Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment”. In: 20th IFAC Symposium on System Identification. 2024. [Ref.]

2. S. A. Dorado-Rojas, T. Bogodorova, and L. Vanfretti. “Time Series-Based Small-Signal Stability Assessment using Deep Learning”. In: 2021 North American Power Symposium (NAPS). 2021, pp. 1–6. DOI: 10.1109/NAPS52732.2021.9654643. [Ref.]

3. F. Fachini, M. De Castro, M. Liu, T. Bogodorova, L. Vanfretti, and W. Zuo. “Multi-Domain Power and Thermo-Fluid System Stability Modeling using Modelica and OpenIPSL”. In: 2022 IEEE Power & Energy Society General Meeting (PESGM). IEEE. 2022, pp. 1–5. DOI: 10.1109/PESGM48719.2022.9917073. [Ref.]

4. R. Kuiava, T. Bogodorova, T. CC. Fernandes, and R. A. Ramos. “A Study on the Relation between the Maximum Loadability Point and Undervoltage Load Shedding Schemes”. In: 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE. 2020, pp. 1–5. DOI: 10.1109/PESGM41954.2020.9281752. [Ref.]

 

For more details you are welcome to follow my Google Scholar page:  Tetiana Bogodorova, Ph.D.