Power System Governing -- the Problem

Dougherty, J. J.; Hayward, A. P.; Monteith, A. C.; Griscom, S. B.

doi:10.1109/t-aiee.1941.5058373

Cited by 8 publications

(2 citation statements)

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“…In 1973, R.H. Park published a paper [23] that provides a detailed description of using ST FV to maintain EPS stability. Historically, the initial studies exploring the use of ST FV date back to 1925 [24] and 1928 [25]. Subsequently, the paper [26] highlights the advantages of using ST FV with a delayed opening of the IV:…”

Section: Related Researchmentioning

confidence: 99%

“…Figure 2 shows the following sections of the ST FV process: open position IV, closing process IV, the delay of a closed position IV for the time T FV , opening the IV for time T o , and open position IV.In 1973, R.H. Park published a paper[23] that provides a detailed description of using ST FV to maintain EPS stability. Historically, the initial studies exploring the use of ST FV date back to 1925[24] and 1928[25]. Subsequently, the paper[26] highlights the advantages of using ST FV with a delayed opening of the IV:…”

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Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Mathematics

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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.

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Section: Related Researchmentioning

confidence: 99%

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confidence: 99%