Steam Turbine Performance Research Articles

Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD

A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency of adjusted hydraulic servomotors, this study proposes a model for detecting abnormalities of hydraulically adjusted servomotors. This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. Then, this model was used as a feature extractor to create a feature set of normal data. Finally, an abnormal detection model for the hydraulically adjusted servomotor was constructed by optimizing the support vector data domain based on this feature set using a genetic algorithm. The proposed method was experimentally validated on a hydraulically adjusted servomotor dataset. The results showed that, compared with the traditional single-scale one-dimensional residual neural network, the multi-scale feature vectors fused by the multi-scale one-dimensional convolutional neural network contained richer state-sensitive information, effectively improving the performance of detecting abnormalities in the hydraulically adjusted servomotor.

Hyper-reduced-order model for estimating convection heat transfer coefficients of turbine rotors

Forced convection heat transfer occurs inside the steam turbine, and it is crucial for thermal and strength analysis to accurately estimate the convection heat transfer coefficients of the turbine rotor. This paper presents a hyper-reduced-order model for efficiently estimating the forced convection heat transfer coefficients of a steam turbine rotor. Unlike the full-order finite element method, the hyper-reduced-order model uses the discrete empirical interpolation method to approximate the domain integration of transient heat conduction simulations. This approach greatly reduces the degrees of freedom of numerical integration, thus substantially improving the computational efficiency of forward heat conduction problems. The hyper-reduced-order model and Levenberg–Marquardt algorithm are combined to minimize temperature errors between the numerical simulation results and remote sensor measurements and iteratively estimate the heat transfer coefficients of forced convection in the rotor. The accuracy and efficiency of the proposed model are verified through a transient heat conduction simulation. Moreover, the effects of the initial guess values and measurement errors are investigated to demonstrate the universality and robustness of the proposed approach in determining the convection heat transfer coefficient. Compared with the full-order finite element model, the proposed hyper-reduced-order model can increase the efficiency of forward heat conduction simulations of the turbine rotor by more than 10 times, and it only takes 0.87 s computer runtime in single transient step simulation, and then rapidly estimate the forced convection heat transfer coefficients. Furthermore, the performance of the proposed approach is insensitive to the initial guess values, and it exhibits great robustness under measurement error. The mean errors are 4.78 %, 2.67 % and 10.51 % when the initial values are 0.015 mW·mm−3·°C−1, 0.05 mW·mm−3·°C−1 and 0.15 mW·mm−3·°C−1 respectively. When there is no measurement error, the estimated error of the convection heat transfer coefficients is 2.67 %, and even if the measurement error reaches 5 %, and the accuracy loss is only 5.57 %. Consequently, this approach is highly suitable for estimating convection heat transfer coefficients during the dynamic operation of steam turbines, and this research provides reliable guidance for the optimization design and safe operation of steam turbines.

Study of the efficiency of the joint operation of a cogeneration steam turbine with a heat pump installation

Study of the efficiency of the joint operation of a cogeneration steam turbine with a heat pump installation

Prediction of Steam Turbine Blade Erosion Using Computational Fluid Dynamics Simulation Data and Hierarchical Machine Learning

Abstract The information of the degree of blade erosion is vital for the efficient operation of steam turbines. However, it is nearly impossible to directly measure the degree of blade erosion during operation. Moreover, collecting sufficient data of eroded cases for predictive analysis is challenging. Therefore, this paper proposes a blade erosion prediction method using numerical simulation and machine learning. Pressure data of several blade erosion cases are collected from the numerical turbine simulation. The machine learning approach involves training on collected simulation data to predict the degree of erosion for the first-stage stator (1S) and the first-stage rotor blade (1R) from internal pressure data. The proposed erosion prediction model employs a two-step hierarchical approach. First, the proposed model predicts the 1S erosion degree using the k-nearest neighbor (k-NN) regression. Second, the proposed model estimates the 1R erosion degree with linear regression models. These models are tailored for each of the 1S erosion degrees, utilizing pressure data processed through fast Fourier transform (FFT). The evaluation shows that the proposed method achieves the prediction of the 1S erosion with a mean absolute error (MAE) of 0.000693 mm and the 1R erosion with an MAE of 0.458 mm. The evaluation results indicate that the proposed method can accurately capture the degree of turbine blade erosion from internal pressure data. As a result, the proposed method suggests that the erosion prediction method can be effectively used to determine the optimal timing for maintenance, repair, and overhaul (MRO).

Analysis of low frequency pulsation and profile modification of turbine control valve

During the operation of a nuclear power steam turbine's main control valve, the valve frequently operates at a small opening and pressure ratio, causing intense alternating loads and oscillation. To address this, a transient numerical simulation studied low-frequency pulsation and optimized steam. Results showed high amplitude and wide frequency range flow pulsations on the spool and tube wall due to repeated flow field separation. By improving the valve core profile, control over the valve throat jet state reduced pressure pulsation amplitudes significantly, as well as mitigating flow-induced vibration in the valve core cavity and pipeline.

Structural Analysis & Effect of Impingement Cooling Flow in the Internal Surface on Temperature Distribution of a Vane in Gas Turbine Using Taguchi Technique

Abstract: Gas turbine always consider one of the most important systems in the modern engineering applications, because it has continuous ability to generate electric power. In gas turbines the major portion of performance dependency lies upon turbine blade design ,the blades are considered one of the important and expensive parts in the gas turbines , where the blades of first stage from failure .The blades of the gas turbine suffer from tensile stresses due to centrifugal forces resulting from the high rotational speed and because of the loading of densegasses at a high temperature and speed ,as the centrifugal force is one of the problems that thedesigner of the turbine blades faces , as the designer aims to reduce stresses within the permissible limit. CFD study was carried out for evaluating the performance of a utility Steam Turbine. The flow in a turbine blade passage is complex and involves understanding of energy conversion in three dimensional geometries. The performance of turbine depends on efficient energy conversion and analyzing the flow path behavior in the various componentsof Steam Turbine. This study seeks the optimization of an axial turbine from a small gas turbine engine developed by ITA using Computational Fluid Dynamics (CFD) and Multi- Objective Optimization techniques focused on geometry changes to maximize the turbine performance. The simulation process will be done through the use of the commercial software SolidWorks.

Modeling combined-cycle power plants in a detailed electricity market model

The efficiency and flexibility of combined-cycle gas turbines (CCGT) are gaining importance in view of more decarbonized energy systems. Therefore, an adequate representation of CCGTs in large-scale energy system models is required. When modeling large-scale energy systems, a trade-off between detailed representation of the technical restrictions and reasonable computing times emerges because of limited computing capacities.We present an approach that accounts for the operational dependencies between the gas and steam turbines of a CCGT but remains applicable in large-scale market models. Duct burners and bypasses that enable the solo operation of steam or gas turbines are also modeled. We implement our approach using the well-established Joint Market Model and apply it in a case study of the German electricity system in 2040.Our modeling approach captures more detailed operation modes, while increases in computation time remain limited. Thus, the approach is advantageous when turbine-wise modeling and analysis of CCGTs is required, e.g. for a detailed analysis of start-ups and operations. The results show no substantial differences between the aggregated market results of five cases investigated. The consideration of bypasses and duct burners yet leads to higher contribution margins for individual CCGTs – with stronger effects observed for more efficient groups.

Evaluation of a New Droplet Growth Model for Small Droplets in Condensing Steam Flows

As the condensation phenomenon occurs in the low-pressure stages of steam turbines, an accurate modelling of the condensing flows is very crucial and has a significant impact on the development of highly efficient steam turbines. In order to accurately simulate condensing steam flows, it is essential to choose the right condensation model. Further research to enhance condensation models is of special importance because the outcomes of numerical studies of condensation models in recent years have not been entirely compatible with the experiments and there are still uncertainties in this area. Therefore, the main aim of this paper is to evaluate a proposed droplet growth model for modelling condensation phenomenon in condensing steam flows. The new model is derived to profit from the advantages of models based on the continuum approach for large droplets and those based on the kinetic theorem for small droplets, which results in the model being robust for a wide range of Knudsen numbers. The model is implemented into a commercial CFD tool, ANSYS Fluent 2022 R1, using UDFs. The results of the CFD simulations are validated against experimental data for linear cascades within the rotor and stator blade geometries of low-pressure steam turbine stages. The findings clearly demonstrate the superiority of the new model in capturing droplet growth, particularly for very small droplets immediately following nucleation. In contrast, widely used alternative droplet growth models tend to either underpredict or overpredict the droplet growth rate. This research significantly contributes to the ongoing efforts to enhance condensation modeling, providing a more accurate tool for optimizing the design and operation of low-pressure steam turbines, ultimately leading to a higher energy efficiency and a reduced environmental impact.

Numerical study of the heterogeneous condensation effect on the steam turbine performance

The investigation of the loss and efficiency of steam turbine holds immense significance in improving the production of electric energy as a pivotal power conversion device in the electric power industry. However, during the expansion of steam in the steam turbine, the existence of heterogeneous particles leads to the heterogeneous condensation, resulting in a significant reduction in the turbine efficiency and safety of its operation. This study investigates the impact of heterogeneous condensation flow on the performance of steam turbines. First, a condensation model is developed, and numerical calculations are performed using the Bakhtar stator blade cascade. The validity of the proposed model is verified by comparing its results with existing experimental data. Then, the adiabatic flow (non-condensing), the homogeneous condensation flow, and the heterogeneous condensation flow on solid particles with a radius of 10-8[m] and particle concentration of 1015 and 1016[1/kg] are employed to investigate the effect of each flow type on steam turbine performance, and the loss, power, and efficiency in the turbine are detailedly and quantitatively calculated. The results show that in the presence of heterogeneous particles, increasing particle concentration appropriately can effectively reduce the loss caused by condensation and improve thermal efficiency.

Simulation model of 177 MWe coal-fired double power unit

Model-based studies are widely used in the analysis and optimization of complex energy conversion processes in large thermal power plants. The creation and validation of a model of a power unit utilizing Bulgarian lignite enables us to study in detail the redistribution of heat flows in that unit. Along with the ability to optimize the thermodynamic cycle of energy generation, this research method enables us to conduct many study tasks that cannot be realized by conducting expensive and complex industrial experiments. The current article presents the creation of thermodynamic simulation model of a 4th power unit firing lignite coal in Maritsa East 2 TPP. Validation of the numerical solution was performed with data from the "Operating Instructions for the boilers”, design heat balance calculations for steam turbine installation and performance test results onsite on two different loads.

A Comparison of Steam Turbine Control Valve Geometries and Their Dynamic Behavior at Part Load

A growing significance of flexible steam turbine operation challenges the control of turbines, as part load operation using control valves can be accompanied by highly unsteady flow conditions. The increased dynamic load induced by pressure forces can reduce the reliable operating range, weaken the valve, and lead to mechanical failures. The geometry of the valve plays a major role in the reduction of dynamic forces. Using a scaled control valve, experiments were conducted with a focus on the dynamic behavior of the valve head. A spherical valve shape favoring unstable operation was used as a reference case, and the desired instability was proven by measurements. Different modified valve geometries based on literature featuring separation edges were then tested against the spherical shape. Results indicate the improved stability of the modified geometries over the reference geometry. For most of the operating range, vibrations were drastically reduced, and the overall flow stabilized.

Co-gasification of biomass and plastic waste for green and blue hydrogen Production: Novel process development, economic, exergy, advanced exergy, and exergoeconomics analysis

A novel co-gasification process for biomass and plastic waste has been proposed to produce the blue and green hydrogen. For process feasibility, an Aspen Plus simulation model has been developed, and a sustainability analysis is being conducted, focusing on economic viability, exergy, advanced exergy considerations, and exergoeconomics evaluations. The current process has demonstrated economic sustainability, as evidenced by an internal rate of return (IRR) of 8 % at a process efficiency level of 70 %. The process with a waste capacity of 20 tons per hour has the potential to produce approximately 1079 kW-hours of electric power. The surplus electricity, exceeding the process requirements is utilized for green hydrogen production through an alkaline electrolysis cell (AEC). This surplus electricity has the potential to produce around 213.5 kg/day of hydrogen. The exergy analysis of this model highlights that the gasifier component exhibits the lowest exergy efficiency, resulting in the highest exergy loss, around 40 %. Furthermore, advanced exergy analysis identifies both the steam turbine and gasifier as primary sources of exergy destruction, with associated exergoeconomics costs of around $6,561.3 and $6,541.9 per hour, respectively. Consequently, improving the gasifier and steam turbine performance can enhance the overall sustainability of the process.

Python-based energy and exergy analysis for efficient evaluation of steam turbine performance

Gas and Steam Power Plant (PLTGU) is a thermal power plant that has been in use since 1901. Over time, the energy and exergy analysis of PLTGU must be a fast analysis and takes very little time. This study aims to analyze the energy and exergy of one of the PLTGU components, namely the steam turbine using the python programming language, so that the analysis can be carried out quickly. As a theoretical basis to be able to carry out energy and exergy analysis of PLTGU steam turbines. The objects analyzed are: system input energy, isentropic efficiency, turbine work, energy loss rate, turbine energy efficiency, system exergy, fuel and product exergy, exergy destruction rate, and exergy efficiency. In carrying out the analysis, the theory used is the laws of thermodynamics 1 and 2. The results of the analysis are carried out according to the adopted theory, the isentropic efficiency of the steam turbine is in the range of to , the exergy of the system will always be greater than the energy of the system, the exergy of fuel is greater than product exergy, and the exergy efficiency of steam turbines is above . It can be concluded from this research, that the steam turbine can still be operated for the next few years.

Exergy Audit of Thermodynamic Parameters and Performance Analysis of OgorodeThermal Power Plant in Delta State, Nigeria.

The exergy analysis of the Ogorode steam plant is presented. The aim was to determine and identify the magnitudes and locations of real exergy losses in order to improve plant efficiency. The exergy losses occurred in the various components of the steam plant, such as the boiler, economizer, turbine, super heater, condenser, pump, feed water heater, reheater, and air pre-heater, and these have been calculated using the concepts of availability or available exergy and irreversibility. The temperature and pressure of the plant's running conditions were combined to determine the process irreversibility (exergy losses) and the exergy efficiency of the plant. The exergy losses of the individual components of the plant show that the maximum exergy losses are in steam turbine operation one (STO ) and steam turbine operation two (STO ). STO and STO are 813 kJ/kg, 1 2 1 2 820 kJ/kg, 822 kJ/kg, and 815 kJ/kg that occurred in the turbine between 2019 and 2021, while the minimum exergy losses in STO and STO are 68 kJ/kg, 150 kJ/kg, 1 2 280 kJ/kg, and 276 kJ/kg that occurred in the boiler system between 2019 and 2021. There was a high energy efficiency of 82% and 80% in the superheater between 2019 and 2021, while 89% in the feed water heater and 73% in the superheater had the same duration. The efficiency of 15% and 21% were the minimum in the pump system between 2019 and 2021, while the turbine had a minimum of 20% and 27% from 2019 to 2021. The highest losses of exergy occurred in the turbine as a result of the irreversibility inherent in the turbine process, corroded turbine blades as a result of wet steam, and low energy delivered to the superheater tubes. It was concluded that the turbine inlet temperature should be increased, more energy delivered to the superheater should be increased, boiler pressure should be increased, and weak feed water heaters should be replaced for better performance of the thermal plant.

Changes in the Thermal and Stress-Strain State of the HPC Rotor of a Powerful NPP Turbine after the Blades Damage

In practice, during the operation of steam turbines, accidental damage to the blades of the rotors and stators of powerful steam turbines occurs. The main causes of emergency stops of steam turbines were vibration fatigue of the blades material, erosive damage to the blades body, and resonance problems during the power equipment operation. Based on this study, the assessment of changes in the thermal and stress-strain state of power equipment elements, which at nuclear power plants significantly affect the continued operation of the turbine after its damage, are quite relevant. Changes in the thermal and stress-strain state, which may occur after damage to the rotor of high-pressure cylinder (HPC rotor) of the K-1000-60/3000 turbine power unit of the LMZ in the station conditions, have been considered and analyzed and will provide an opportunity to assess the individual resource and continue the power unit operation. In the calculated assessment of changes in the thermal and stress-strain state of the HPC rotor, taking into account the data of the technical audit regarding damage, a geometric model of the rotor was created. Studies were conducted for three options of designs: the original option (five stages of the HPC rotor), the option without the blades of the last stage and the option without the fifth stage (with four first stages). For the project design, when working at the nominal parameters of the steam, the most stressed areas are the unloading holes of the 5th stage (σi=202.8 MPa), axial hole of the rotor in the area of the 5th stage (σi=195.2 MPa), as well as the 5th-degree welding fillet from the side of the end seals (σi=200.3 MPa) and unloading holes of the 4th and 3rd stages with a stress intensity of about 170–185 MPa. The high values of the stress intensity in the area of the 5th stage can be explained by the significant concentration of the mass of both the stage itself and its blades, which provoke significant centrifugal forces when working at the nominal rotation frequency. For a HPC rotor without blades of the 5th stage, there is a shift of the maximum stress intensity to the area of the unloading holes of the 4th and 3rd stages, as well as the axial hole of the shaft under the same stages. The maximum stress value is σi max=184.8 MPa. At the same time, the intensity of stresses in the area of unloading holes of the 5th degree decreased almost by half, to the level of 124 MPa.

Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines

Through condition-based maintenance strategy, engineers can monitor the health states of equipment and take actions based on the sensor data. Limited by the low failure frequency and high monitoring costs, it is difficult to obtain sufficient historical data of all fault types for condition monitoring (CM). In the steam turbine operation, environmental factors, varying power consumption and manual adjustments can lead to a multimode process, which consists of multiple normal and abnormal conditions. This paper proposes a framework for online unsupervised CM and anomaly detection, not relying on expert knowledge or labeled historical data. Since there are often few monitoring data at the beginning of a new incoming operating mode, an adaptive self-transfer learning algorithm based on Gaussian processes is developed to model the monitoring data with uncertainty information, and to capture the cross-correlations between the different normal modes. A two-hierarchical identification criterion based on the predicted posterior intervals is introduced to first identify the change-points in the observations, and second to decide whether it is an anomaly or a transition between normal modes. The proposed framework is tested on a real steam turbine. The results illustrate its high effectiveness.

Performance Analysis of Extraction Condensing Turbine - Unit 1 at Pltu X, Bekasi, West Java

In 2014, Cikarang Listrindo Energy built a steam power plant in Babelan to participate in the government programs. In April 2017, Unit 1 of PLTU Babelan has been operated. After the plant has been in operation for 4 months, the plant will have a performance test for the main part of the steam power plant, one of which is the steam turbine performance test. This performance test purpose is to get actual performance data where it will be compared to the design data, and it willl be used for warranty from the steam turbine contractor. The performance test is using ASME PTC 6 for steam turbine performance test. The steam turbine performance test can be seen from several parameters such as test capability, heat rate and turbine efficiency. The results of the steam turbine performance test based on the design are: the output power is 138,010 MW; the heat rate is 8865 kJ/kWh; and the turbine efficiency is 87.38%. Meanwhile the results of the performance test based on commissioning are: the output power is 139,295 M; the heat rate is 8919 kJ/kWh; and the turbine efficiency is 87.03%. The actual performance test results are: the output power is 137,595 MW, the heat rate is 8830.64 kJ/kWh, and the turbine efficiency is 88.65%. The tolerance given from ASME PTC 6 is 2%. Exhaust turbine pressure affect the turbine efficiency, where as lower exhaust turbine pressure causes higher turbine efficiency.

Modeling of the circumferential crack growth under torsional vibrations of steam turbine shafting

Long-term operation of steam turbines has one of its consequences the formation of fatigue cracks of various types in their structural elements. Evidence of this are accidents and catastrophic destructions of steam turbines.Maine reason of long-term accumulation of fatigue damage in steam turbines shafting is torsional vibrations caused by the connection of turbogenerator to an electrical network with an inaccurate synchronization. A methodology is proposed to study the process of circumferential crack growth in the turbine shafting because of many such connections. The methodology is based on the use of the 3D finite element model of the steam turbine K-200–130 shafting and a fracture mechanics approach.There was demonstrated that the inaccuracy of turbogenerator connection to an electric network is the most influential factor affecting the crack growth. The conditions for the crack to reach a critical size has been evaluated. In the problem being considered damping capacity of the rotor steel influences the intensity of crack growth insignificantly.

Optimization of the surface heating for a stationary cascade turbine blade in wet steam flow

The purpose of this study is to obtain the optimal surface heating for a stationary cascade turbine blade in wet steam flow by a genetic algorithm. The numerical method is conducted by employing two-dimensional Navier–Stokes equations coupled with a SSTk-ω turbulence model. Nucleation and droplet growth equations are solved using the Eulerian-Eulerian approach. The numerical results show good agreement with well-established experiments. Nozzle efficiency (NE), integral of local entropy changes at the outlet (ILE), mean wetness at the outlet (MWO), mean momentum at the outlet (MMO), and cost price (CP) are objective functions. The ultimate purpose is to minimize the (ILE), (MWO), and (CP) and maximize the (NE) and (MMO) together. Since higher surface heating rates decrease MWO and MMO, while increasing ILE, CP, and NE based on optimization results, there is an optimum for the surface heating rate to gain the best performance of steam turbines. According to the numerical results, the optimum – is equal to 0.04467 (kWcm2). In the optimal case compared to the non-heat case, NE and MWO are improved 0.26% and 19.94%, respectively. In addition, the ILE and MMO are degraded 0.9%, 0.32%, respectively, and CP is estimated 0.0027 ($cm2.h).

Numerical analysis of hot steam injection through an embedded channel inside a 3D steam turbine blade

The steam expands rapidly and releases its latent heat at the last stages of steam turbines, forming a large number of tiny liquid droplets. This non-adiabatic process results in a sudden phenomenon known as condensation shock, leading to significant thermodynamic losses. Given that the condensation phenomenon can reduce the performance of steam turbines and shorten their lifespan, several studies in recent years have been proposed to eliminate liquid droplets, some focusing on surface heating, others on hot steam injection. However, providing a practical platform design for hot steam injection remains to be studied, which defines the primary goal of this investigation. In this research, a channel is installed inside the 3D turbine blade, allowing hot steam to enter and be stored there. Then, the stored hot steam enters the mainstream flow through eight injection holes with unequal mass flow rates from each hole. Six criteria are defined to see how hot steam injection contributes to decreasing wet steam losses and affects other key parameters. The results of CFD simulations indicate that hot steam injection reduces the rate of erosion, condensation losses, and wetness, respectively, by 58%, 28.5%, and 31.5%. Notwithstanding the advantages of this method, kinetic energy decreases by about 0.5%. This, however, seems to be acceptable due to the remarkable reduction of erosion and condensation losses. Finally, the obtained results indicated the fact that injecting hot steam through the designed channel costs roughly 0.45 ($/hour).