Some parameters are influenced by the turbine unit's torsional oscillations. The fundamental comes from damping these oscillations, which are brought on by a departure in the turbine blades' speed from the device's prediction of the steam volume, and attenuation of fluctuations due to the distribution of energy in the turbine's productive components. The usual single-machine infinite bus system is used for the analysis. For various turbine-generator shafts and various generator operating situations, rotating mass mechanical system evaluations for small-signal stability and large disturbance are conducted. It is demonstrated that the shaft's "structural' damping (H) and "steam' damping (Kn) coefficients have a considerable impact on the damping of torsional modes. The goal of this work is to determine the effect of changing the damping factors in the mathematical model of the steam turbine shaft on the system's static stability, as well as the extent to which these variables' limits on damping rotational oscillations on the maximum torsional torques generated in the shaft masses. The mathematical model of the steam turbine shaft with a single machine and transmission line to an infinite bus system was simulated using Dymola software, and the static and dynamic effects of damping factors (H) and (Kn) on system stability were demonstrated. By evaluating the best case for parameters with the least influence on the system's stability, the results were obtained by changing the factors (Kn) from 0.005 to 0.5 and (H) from 0.005 to 0.2 and the extent of its effect on the maximum torque of the steam turbine masses and reducing it by 8.4 %, as well as by reducing the settling time of the system after disturbances occur and reaching to Steady state by about 90 %.
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http://dx.doi.org/10.1016/j.heliyon.2024.e23995 | DOI Listing |
Sensors (Basel)
January 2025
Institute of Power Plant Technology, Steam and Gas Turbines, RWTH Aachen University, 52062 Aachen, Germany.
Synchronous vibrations, which are caused by periodic excitations, can have a severe impact on the service life of impellers. Blade Tip Timing (BTT) is a promising technique for monitoring synchronous vibrations due to its non-intrusive nature and ability to monitor all blades at once. BTT generally employs a Once-per-Revolution (OPR) sensor that is mounted on the shaft for blade identification and deflection calculation.
View Article and Find Full Text PDFSci Rep
January 2025
ENET Centre, VSB-Technical University of Ostrava, Ostrava, 708 00, Czech Republic.
Steam condensers are vital components of thermal power plants, responsible for converting turbine exhaust steam back into water for reuse in the power generation cycle. Effective pressure regulation is crucial to ensure operational efficiency and equipment safety. However, conventional control strategies, such as PI, PI-PDn and FOPID controllers, often struggle to manage the nonlinearities and disturbances inherent in steam condenser systems.
View Article and Find Full Text PDFMaterials (Basel)
December 2024
Department of Power Systems and Environmental Protection Facilities, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland.
This study explores the impact of structural modifications on the stress distribution and flow characteristics of a nozzle box in a steam turbine, specifically targeting the components made from high-strength StE460 steel. Using Computational Fluid Dynamics (CFDs) and Thermal-Fluid-Structure Interaction (Thermal-FSI) simulations, we examine the effects of shortening the nozzle guide vanes by 7 mm. This novel approach significantly reduces the stress levels within the nozzle box segments, bringing them below the critical thresholds and thus enhancing component durability.
View Article and Find Full Text PDFEntropy (Basel)
November 2024
China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China.
Steam turbine blades may crack, break, or suffer other failures due to high temperatures, high pressures, and high-speed rotation, which seriously threatens the safety and reliability of the equipment. The signal characteristics of different fault types are slightly different, making it difficult to accurately classify the faults of rotating blades directly through vibration signals. This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM).
View Article and Find Full Text PDFHeliyon
November 2024
Department of Petroleum and Mining Engineering, Military Institute of Science and Technology (MIST), Dhaka-1216, Bangladesh.
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