Resumen

GUANUNA, G.F. et al. Voltage Stability Margin Estimation Using Machine Learning Tools. Revista Técnica energía [online]. 2023, vol.20, n.1, pp.1-8. ISSN 2602-8492.  https://doi.org/10.37116/revistaenergia.v20.n1.2023.570.

Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large volume of information, high execution times and computational cost. Based on this background, this technical work proposes an alternative method for voltage stability margin estimation through the application of artificial intelligence and data mining algorithms. For this purpose, 10 000 operate scenarios were generated through Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of all scenarios were determined using power-voltage (PV) curves in order to obtain a database. This information allowed structuring a data matrix for training and evaluating an artificial neural network and a support vector machine, capable of predicting the voltage stability margin, even in real time. The performance of the prediction tools was evaluated through the mean square error and the coefficient of determination. The proposed methodology was applied to the IEEE 14 bus test system, showing so promising results for both the neural network and the vector machine, where the coefficients of determination were 0.9153 and 0.8317, respectively.

Palabras clave : Voltage stability assessment; Monte Carlo method; voltage stability margin estimation; artificial intelligence algorithms.

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