Voltage Stability Margin Estimation Using Machine Learning Tools
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Abstract
Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results.
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References
J. Rueda et al., “Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems,” Wiley-IEEE Press, 2018.
C. Andersson, J. E. Solem, and B. Eliasson, “Classification of power system stability using support vector machines,” 2005 IEEE Power Eng. Soc. Gen. Meet., vol. 1, no. 2, pp. 650–655, 2005, doi: 10.1109/pes.2005.1489266.
J. Cepeda, P. Verdugo, and G. Argüello, “Monitoreo de la Estabilidad de Voltaje de Corredores de Transmisión en Tiempo Real a partir de Mediciones Sincrofasoriales,” Rev. EPN, vol. 33, no. 3, 2014.
N. P. Patidar and J. Sharma, “Loadability margin estimation of power system using model trees,” 2006 IEEE Power India Conf., pp. 338–343, 2005, doi: 10.1109/POWERI.2006.1632534.
M. Bilgen and S. Ozdemir, “Comparison of Real-Time Voltage Stability Assessment Methods,” 2021 13th Int. Conf. Electr. Electron. Eng., pp. 73–77, 2021, doi: 10.23919/ELECO54474.2021.9677734.
A. Adhikari, S. Naetiladdanon, A. Sagswang, and S. Gurung, “Comparison of voltage stability assessment using different machine learning algorithms,” 2020 IEEE 4th Conf. Energy Internet Energy Syst. Integr. Connect. Grids Towar. a Low-Carbon High-Efficiency Energy Syst. EI2 2020, pp. 2023–2026, 2020, doi: 10.1109/EI250167.2020.9346750.
M. Zhang, J. Li, Y. Li, and R. Xu, “Deep Learning for Short-Term Voltage Stability Assessment of Power Systems,” IEEE Access, vol. 9, pp. 29711–29718, 2021.
N. Hatziargyriou et al., “Definition and Classification of Power System Stability - Revisited & Extended,” IEEE Trans. Power Syst., vol. 36, no. 4, pp. 3271–3281, 2021, doi: 10.1109/TPWRS.2020.3041774.
M. Amroune, “Machine Learning Techniques Applied to On-Line Voltage Stability Assessment: A Review,” Arch. Comput. Methods Eng., vol. 28, no. 2, pp. 273–287, 2021, doi: 10.1007/s11831-019-09368-2.
P. Kundur, Power System Stability And Control. McGraw-Hill, 1994.
A. Reddy, K. Ekmen, V. Ajjarapu, and U. Vaidya, “PMU based real-time short term voltage stability monitoring - Analysis and implementation on a real-time test bed,” 2014 North Am. Power Symp. NAPS 2014, 2014, doi: 10.1109/NAPS.2014.6965485.
Y. Lee and S. Han, “Real-time voltage stability assessment method for the Korean power system based on estimation of Thévenin equivalent impedance,” Appl. Sci., vol. 9, no. 8, 2019, doi: 10.3390/app9081671.
G. F. Patiño and G. A. Limas, “Metodologías para el análisis de estabilidad de tensión en estado estacionario,” Universidad Tecnológica de Pereira, 2008.
L. Chiza and J. Cepeda, “Predicción del Margen de Estabilidad de Corredores de Transmisión Aplicando Criterios de Minería de datos y Algoritmos de Machine Learning,” Rev. Técnica “energía,” vol. 18, no. 1, pp. 37–47, 2021, doi: 10.37116/revistaenergia.v18.n1.2021.466.
Y. Narcisse and N. Tchokonte, “Real time identification and monitoring of the voltage stability margin in electric power transmission systems using synchronized phasor measurements,” p. 344, 2009.
DIgSILENT GmbH, “PowerFactory 2021 - User Manual,” pp. 1–28, 2021.
B. Marah and A. O. Ekwue, “Probabilistic load flows,” Proc. Univ. Power Eng. Conf., vol. 2015-Novem, no. 1, 2015.
M. S. Chamba, W. A. Vargas, and J. Cristobal Cepeda, “Stochastic assessment and risk management of transient stability based on powerfactory and python interface,” 2020 IEEE PES Transm. Distrib. Conf. Exhib. - Lat. Am. T D LA 2020, 2020.
P. Flach, Data, Machine Learning: The Art and Science of Algorithms that Make Sense of. Cambridge University Press, 2012.
K. D. Dharmapala, A. Rajapakse, K. Narendra, and Y. Zhang, “Machine Learning Based Real-Time Monitoring of Long-Term Voltage Stability Using Voltage Stability Indices,” IEEE Access, vol. 8, pp. 222544–222555, 2020.
W. M. Villa-Acevedo, J. M. López-Lezama, and D. G. Colomé, “Voltage stability margin index estimation using a hybrid kernel extreme learning machine approach,” Energies, vol. 13, no. 4, 2020, doi: 10.3390/en13040857.
O. A. Alimi, K. Ouahada, and A. M. Abu-Mahfouz, “A Review of Machine Learning Approaches to Power System Security and Stability,” IEEE Access, vol. 8, pp. 113512–113531, 2020, doi: 10.1109/ACCESS.2020.3003568.
D. E. Echeverría, “Evaluación y mejora de la estabilidad transitoria de sistema eléctricos en tiempo real utilizando PMUs,” Universidad Nacional de San Juan, 2021.
R. Lincoln, “PYPOWER,” 2017. https://github.com/rwl/PYPOWER.
F. Predregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011, [Online]. Available: https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation.
S. Kokoska and D. Zwillinger, Standard Probability and Statistics Tables and Formulae. New York, 2000.
