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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. [ABSTRACT FROM AUTHOR]

At present, due to the increase and significant complication of the electric power system (EPS), the task of maintaining the stability of the EPS is becoming increasingly important. Due to the fact that, in case of significant disturbances in the EPS, the processes occurring can lead to large synchronous swings of the generating equipment or loss of stability. When disconnecting the intersystem communication or a powerful load in the excess part of the EPS, an emergency excess of generated active power occurs, as a result, the frequency begins to increase, and the load-frequency control system is not able to cope with such an accident, due to its slowness. In this case, it is necessary to use the means of emergency automation to eliminate the surplus of active power. One of the most effective methods is fast valving automation. However, its effectiveness directly depends on the quality of its setting - which is a rather difficult task, since for its implementation, a complete mathematical model of the turbo-generator is required, taking into account its control systems and boiler unit. [ABSTRACT FROM AUTHOR]

Highlights: [·] An application method for the intelligent SPS is proposed. [·] The number of tripped generators is minimized in the intelligent SPS. [·] Fast valving and braking resistor are used to improve the transient stability. [·] The results can be applied to ensure effective operation of the KEPCO system. [ABSTRACT FROM AUTHOR]