scholarly journals Energy Efficient Strategy Development of Steam Turbine through Vibration Reduction Using ANN and SVM Approaches

2022 ◽  
Vol 12 (1) ◽  
pp. 65
Author(s):  
Yasir Rafique ◽  
Abid Hussain
Keyword(s):  
Power Plant ◽  
Steam Turbine ◽  
High Speed ◽  
Process Model ◽  
External Validation ◽  
Vibration Reduction ◽  
Strategy Development ◽  
Ann Model ◽  
Load Range ◽  
Bearing Vibration

The energy efficiency of a power plant is largely determined by the vibrations of bearings that hold the shaft rotating at high speed which need to be critically controlled. This study presents the relative vibration modeling of a shaft bearing that is installed in a 660 MW supercritical steam turbine system. The operational data in raw form after being cleaned using machine learning based visualization and extensive data processing helped in training and validation of SVM and ANN models which are then compared by external validation tests. The model with best results is then used for the simulations of constructed operating scenarios. The ANN has been further tested for the complete operational load range (353 MW to 662 MW) which predicted the reduction in relative vibrations. Moreover, the validated ANN model has been used to develop many strategies of vibration reduction which helped in achieving more than 4% reduction in relative vibrations. Subsequently, an operational strategy that predicts a significant reduction in the bearing vibration levels is selected. For confirmation of the accuracy of prediction by ANN process model, the selected strategy has been used with the actual power plant. This assures the significant reduction of bearing vibration less than the alarm limit.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

Paper 23: The Collision of Drops with Dry and Wet Surfaces in an Air Atmosphere

1969 ◽  
Vol 184 (7) ◽  
pp. 57-63
Author(s):  
G. J. Parker ◽  
E. Bruen
Keyword(s):  
Steam Turbine ◽  
High Speed ◽  
Size Range ◽  
Drop Surface ◽  
Wet Steam ◽  
Air Atmosphere ◽  
Made In

This paper describes an investigation into the behaviour of drops which impinge upon dry and wet surfaces. This is of particular interest in the context of the wet steam turbine. Two approaches have been made in the studies; these are: (1) Drops were made to impinge normally on to various types of dry, stationary surfaces. The drops were in the size range 300–1500 μm diameter with velocities of 2–9 m/s. (2) Drops were made to impinge on to surfaces moving with considerable velocity at right angles to the motion of the drop. Surface velocities ranged up to 45 m/s. The latter study is of direct interest for the splashing of drops on turbine casings at small glancing angles, as occurs near drainage belts. Analysis of the mechanisms involved is made from the records of high-speed ciné photography.

Seawater Scrubbing for the Removal of Sulfur Dioxide in a Steam Turbine Power Plant

2005 ◽  
Author(s):  
Akili D. Khawaji ◽  
Jong-Mihn Wie
Keyword(s):  
Power Plant ◽  
Sulfur Dioxide ◽  
Steam Turbine ◽  
Flue Gas ◽  
Operating Cost ◽  
Cooling Water ◽  
Cost Savings ◽  
Fuel Oil ◽  
So2 Emissions ◽  
Heavy Fuel Oil

The most popular method of controlling sulfur dioxide (SO2) emissions in a steam turbine power plant is a flue gas desulfurization (FGD) process that uses lime/limestone scrubbing. Another relatively newer FGD technology is to use seawater as a scrubbing medium to absorb SO2 by utilizing the alkalinity present in seawater. This seawater scrubbing FGD process is viable and attractive when a sufficient quantity of seawater is available as a spent cooling water within reasonable proximity to the FGD scrubber. In this process the SO2 gas in the flue gas is absorbed by seawater in an absorber and subsequently oxidized to sulfate by additional seawater. The benefits of the seawater FGD process over the lime/limestone process and other processes are; 1) The process does not require reagents for scrubbing as only seawater and air are needed, thereby reducing the plant operating cost significantly, and 2) No solid waste and sludge are generated, eliminating waste disposal, resulting in substantial cost savings and increasing plant operating reliability. This paper reviews the thermodynamic aspects of the SO2 and seawater system, basic process principles and chemistry, major unit operations consisting of absorption, oxidation and neutralization, plant operation and performance, cost estimates for a typical seawater FGD plant, and pertinent environmental issues and impacts. In addition, the paper presents the major design features of a seawater FGD scrubber for the 130 MW oil fired steam turbine power plant that is under construction in Madinat Yanbu Al-Sinaiyah, Saudi Arabia. The scrubber with the power plant designed for burning heavy fuel oil containing 4% sulfur by weight, is designed to reduce the SO2 level in flue gas to 425 ng/J from 1,957 ng/J.

scholarly journals Increase of power and efficiency of the 900 MW supercritical power plant through incorporation of the ORC

2013 ◽  
Vol 34 (4) ◽  
pp. 51-71 ◽  
Author(s):  
Paweł Ziółkowski ◽  
Dariusz Mikielewicz ◽  
Jarosław Mikielewicz
Keyword(s):  
Power Plant ◽  
Heat Source ◽  
Steam Turbine ◽  
Hybrid System ◽  
Heat Recovery ◽  
Reference Conditions ◽  
Hot Water ◽  
Operational Parameters ◽  
Reference Case ◽  
Unit Power

Abstract The objective of the paper is to analyse thermodynamical and operational parameters of the supercritical power plant with reference conditions as well as following the introduction of the hybrid system incorporating ORC. In ORC the upper heat source is a stream of hot water from the system of heat recovery having temperature of 90 °C, which is additionally aided by heat from the bleeds of the steam turbine. Thermodynamical analysis of the supercritical plant with and without incorporation of ORC was accomplished using computational flow mechanics numerical codes. Investigated were six working fluids such as propane, isobutane, pentane, ethanol, R236ea and R245fa. In the course of calculations determined were primarily the increase of the unit power and efficiency for the reference case and that with the ORC.

Steam Turbine Driving Compressor for Gas-Steam Combined Cycle Power Plant

2009 ◽  
Author(s):  
Wancai Liu ◽  
Hui Zhang
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Steam Turbine ◽  
Gas Turbine ◽  
Combined Cycle ◽  
Thermodynamic Process ◽  
Combined Cycle Power Plant ◽  
Steam Cycle

Gas turbine is widely applied in power-generation field, especially combined gas-steam cycle. In this paper, the new scheme of steam turbine driving compressor is investigated aiming at the gas-steam combined cycle power plant. Under calculating the thermodynamic process, the new scheme is compared with the scheme of conventional gas-steam combined cycle, pointing its main merits and shortcomings. At the same time, two improved schemes of steam turbine driving compressor are discussed.

Modeling of a Steam Turbine Including Partial Arc Admission for Use in a Process Simulation Software Environment

2011 ◽  
Author(s):  
Eric Liese
Keyword(s):  
Steam Turbine ◽  
Process Simulation ◽  
Process Model ◽  
Constant Pressure ◽  
Pressure Ratio ◽  
Simulation Software ◽  
State Variables ◽  
Steam Turbines ◽  
Software Environment ◽  
Last Stage

A dynamic process model of a steam turbine, including partial arc admission operation, is presented. Models were made for the first stage and last stage, with the middle stages presently assumed to have a constant pressure ratio and efficiency. A condenser model is also presented. The paper discusses the function and importance of the steam turbines entrance design and the first stage. The results for steam turbines with a partial arc entrance are shown, and compare well with experimental data available in the literature, in particular, the “valve loop” behavior as the steam flow rate is reduced. This is important to model correctly since it significantly influences the downstream state variables of the steam, and thus the characteristic of the entire steam turbine, e.g., state conditions at extractions, overall turbine flow, and condenser behavior. The importance of the last stage (the stage just upstream of the condenser) in determining the overall flowrate and exhaust conditions to the condenser is described and shown via results.

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