Turbine Fast Valving Study by the Use of a Steam Turbine Real-Time Simulator

Kielian, Maciej; Lis, Robert

doi:10.1109/electronics.2018.8443643

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Fast control equipment not only ensures the stable operation of the power system after the accident, but also prevents the turbine thermal system and turbine body from being damaged. 1–3 Fast control is a special transient operation condition of a steam turbine, which includes two processes: fast closing and fast opening. Fast closing is to reduce the turbine's mechanical output power rapidly by fast closing the steam turbine valve.…”

Section: Introductionmentioning

confidence: 99%

Design, simulation, and experiment research on fast control system of steam turbine inlet valve

Liu

Yin

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this paper, a new fast control system of steam turbine inlet valve is designed, which is composed of the fast closing system and the fast opening system. For the first time, this paper proposes that the fast closing system is designed by means of the special structure of the slide valve in an oil servo motor on the basis of the transformation of the hydraulic control system of steam turbine inlet valve. For the first time, a differential oil discharge fast opening system is designed by use of two cartridge valves. The mathematical model of electro-hydraulic fast control system is presented. With the use of simulation software Advanced Modeling Environment for Simulation of engineering systems , the curves of parameters such as piston displacement, piston velocity, oil pressure of upper and nether cavities of oil servo motor, and flow flux of upper and nether cavities of oil servo motor are obtained. The fast closing time of the piston for whole journey from the simulation results is 0.36 s. The fast opening time of the piston for whole journey from the simulation results is 1.55 s. According to the designed structure of the fast control system, the fast control experiment system is built. The fast closing time of the piston for whole journey from the experiment results is 0.22 s. The fast opening time of the piston for the whole journey from the experiment results is 1.97 s. The simulation and experimental results show that the designed fast control system can realize the fast closing and fast opening of the inlet valve. The fast closing time of the fast control system is <0.5 s, and the fast opening time of the fast control system is <3 s, which meets the fast control time requirement of the steam turbine inlet valve. Compared with the existing fast control system products, the fast control system and the inlet valve servo regulation system share a set of oil sources. And the fast control system has the advantages of low cost, simple structure, easy implementation, etc.

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Section: Introductionmentioning

confidence: 99%

Design, simulation, and experiment research on fast control system of steam turbine inlet valve

Liu

Yin

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The authors of [38] used the optimization method and the equal area criterion (EAC) with testing on the IEEE 39 model to determine the values of µ min and T FV . Also, the direction of studying ST FV contains the following: the analysis of the results of testing on a real-time modeling complex [39], real EPS [40,41], as well as the influence of ST FV on the protection systems of electrical network elements [42].…”

mentioning

confidence: 99%

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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Adaptive Algorithm for Steam Turbine Fast Valving Based on the Equal Area Criterion and Synchrophasor Measurements

Senyuk

Moiseichenkov

Kovalenko

et al. 2021

2021 International Conference on Electrotechnical Complexes and Systems (ICOECS)

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Turbine Fast Valving Study by the Use of a Steam Turbine Real-Time Simulator

Cited by 5 publications

References 9 publications

Design, simulation, and experiment research on fast control system of steam turbine inlet valve

Design, simulation, and experiment research on fast control system of steam turbine inlet valve

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

Adaptive Algorithm for Steam Turbine Fast Valving Based on the Equal Area Criterion and Synchrophasor Measurements

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