Voltage Stability Margin Estimation Using Machine Learning Tools

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.