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This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine’s cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14%. The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research.

Turbine FV is a stability enhancing method used to protect power systems against the loss of synchronism after extreme contingency events. To avoid unnecessary FV action, smart control schemes can be used based on real-time instability prediction. In the last decade, several WAMS-based instability prediction methods have been developed. However, relatively long cumulative latencies in WAMS structures significantly reduce the efficiency of preventive control and can lead to system instability. To speed up FV action initiated by smart control scheme, this paper proposes a fast stability prediction method which is completely different from other methods described in literature. It uses only local measurements and requires performing simple mathematical operations. The basis of this method is the prediction of the magnitude of the power-angle characteristic. Just after the fault clearance, the method allows to predict transient instability and initiate MFV action. Based on the magnitude of the power-angle characteristic, the steady-state stability margin can be also computed. When this margin is too small, SFV action can be initiated as well. The validity of the proposed method has been verified by simulations performed for a large-scale real power system and detailed models of power system elements.