Steam Turbine Loss Evaluation and Condition Monitoring: A Loss Data Based Study

2016 ◽  
Author(s):  
Bin Zhou
Keyword(s):  
Steam Turbine ◽  
Condition Monitoring ◽  
Domain Knowledge ◽  
Loss Reduction ◽  
Steam Turbines ◽  
Monitoring Methods ◽  
Loss Mitigation ◽  
Loss Data ◽  
Effective Condition ◽  
Different Levels

Steam turbine mechanical breakdowns dominate equipment losses in the Power-Gen and Forest Product industries. As steam turbines are likely custom-built, variations in design, operation and maintenance practices across different industries could result in different levels of significance of the loss drivers. The present study focuses on comparing the turbine loss drivers and effective condition monitoring for loss mitigation in both industries. Steam turbine loss events from the two industries during a recent 10-year period were first reviewed and classified into typical turbine loss scenarios. Contributions of each loss scenario to the total loss count and value were summarized and compared across the two industries. Subsequently, applicable turbine condition monitoring methods were identified for each loss scenario, and evaluated with expert domain knowledge and available loss data. These monitoring methods were finally prioritized according to their functional effectiveness in turbine loss reduction.

Effect of Condition Monitoring on Risk Mitigation for Steam Turbines in the Forest Products Industry

2015 ◽  
Author(s):  
Bin Zhou ◽  
Kumar Bhimavarapu
Keyword(s):  
Condition Monitoring ◽  
Domain Knowledge ◽  
Risk Mitigation ◽  
Forest Products ◽  
Event Tree ◽  
Steam Turbines ◽  
Event Tree Analysis ◽  
Monitoring Techniques ◽  
Mechanical Breakdown ◽  
Semi Empirical

Industry has been implementing condition monitoring for turbines to minimize losses and to improve productivity. Deficient conditions can be identified before losses occur by monitoring the equipment parameters. For any loss scenario, the effectiveness of monitoring depends on the stage of the loss scenario when the deficient condition is detected. A scenario-based semi-empirical methodology was developed to assess various types of condition monitoring techniques, by considering their effect on the risk associated with mechanical breakdown of steam turbines in the forest products (FP) industry. A list of typical turbine loss scenarios was first generated by reviewing loss data and leveraging expert domain knowledge. Subsequently, condition monitoring techniques that can mitigate the risk associated with each loss scenario were identified. For each loss scenario, an event tree analysis was used to quantitatively assess the variations in the outcomes due to condition monitoring, and resultant changes in the risk associated with turbine mechanical breakdown. An application was developed following the methodology to evaluate the effect of condition monitoring on turbine risk mitigation.

Steam Turbine Condition Monitoring (TCM)

2007 ◽  
Author(s):  
Christof Nagel
Keyword(s):  
Steam Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Process Parameters ◽  
Early Stage ◽  
Diagnostic System ◽  
Steam Turbines ◽  
Oil Consumption ◽  
Repair Work ◽  
And Performance

Interest in online turbine condition monitoring has increased among utilities in order to minimize unforeseen standstills and for better planning of overhauls or repair work. The AMODIS® (ALSTOM Monitoring and Diagnostic System) Steam Turbine Condition Monitoring system monitors steam turbines locally or remotely via long distances [1]. The system also collects all data to compare current events with past events. This monitoring system is not an expert system recommending how to solve malfunctions. It is more a system which helps operators to take measures before the standard alarm or turbine trip is activated. An interlock of the process parameters generates early warning alarms which are based on the OEM experience and help operators to get a clear picture of an arising problem and to react early enough to avoid forced outages. Additional sensors for additional process parameters have to be installed. The system is part of the AMODIS plant monitoring system and consists of six separately available modules: • Steam inlet valves: To detect increased friction in the actuator and the steam valve guide. • Jacking oil and turning gear: To detect malfunction in the jacking oil and turning gear systems. • Bearing supervision: To detect possible tilting of the bearing pedestal or abnormal oil consumption. • Thermal expansion: To detect extreme or abnormal differential expansion and to detect expansion hindrance. • Thermal efficiency: To detect loss of internal efficiency at an early stage. • Lube oil condition monitoring: To monitor the oil with an online particle counter and a sensor for content of water. All modules can be supplied separately. Modules to check vibration and performance are also available in an AMODIS system but are not covered in this paper.

Effect of Condition Monitoring on Risk Mitigation for Steam Turbines in the Forest Products Industry

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Bin Zhou ◽  
Kumar Bhimavarapu
Keyword(s):  
Condition Monitoring ◽  
Domain Knowledge ◽  
Risk Mitigation ◽  
Forest Products ◽  
Event Tree ◽  
Steam Turbines ◽  
Event Tree Analysis ◽  
Monitoring Techniques ◽  
Mechanical Breakdown ◽  
Semi Empirical

Industry has been implementing condition monitoring (CM) for turbines to minimize losses and to improve productivity. Deficient conditions can be identified before losses occur by monitoring the equipment parameters. For any loss scenario, the effectiveness of monitoring depends on the stage of the loss scenario when the deficient condition is detected. A scenario-based semi-empirical methodology was developed to assess various types of condition monitoring techniques, by considering their effect on the risk associated with mechanical breakdown of steam turbines in the forest products (FPs) industry. A list of typical turbine loss scenarios was first generated by reviewing loss data and leveraging expert domain knowledge. Subsequently, condition monitoring techniques that can mitigate the risk associated with each loss scenario were identified. For each loss scenario, an event tree analysis (ETA) was used to quantitatively assess the variations in the outcomes due to condition monitoring, and resultant changes in the risk associated with turbine mechanical breakdown. An application was developed following the methodology to evaluate the effect of condition monitoring on turbine risk mitigation.

scholarly journals Cost-Effective Condition Monitoring for Wind Turbines

2010 ◽  
Vol 57 (1) ◽  
pp. 263-271 ◽  
Author(s):  
Wenxian Yang ◽  
P.J. Tavner ◽  
C.J. Crabtree ◽  
M. Wilkinson
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Cost Effective ◽  
Effective Condition

Fault diagnosis method of peak-load-regulation steam turbine based on improved PCA-HKNN artificial neural network

2021 ◽  
pp. 1748006X2110105
Author(s):  
Yifan Wu ◽  
Wei Li ◽  
Deren Sheng ◽  
Jianhong Chen ◽  
Zitao Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Power Plants ◽  
Peak Load ◽  
Clean Energy ◽  
Steam Turbines ◽  
Peak Shaving ◽  
Diagnosis Method ◽  
Load Regulation

Clean energy is now developing rapidly, especially in the United States, China, the Britain and the European Union. To ensure the stability of power production and consumption, and to give higher priority to clean energy, it is essential for large power plants to implement peak shaving operation, which means that even the 1000 MW steam turbines in large plants will undertake peak shaving tasks for a long period of time. However, with the peak load regulation, the steam turbines operating in low capacity may be much more likely to cause faults. In this paper, aiming at peak load shaving, a fault diagnosis method of steam turbine vibration has been presented. The major models, namely hierarchy-KNN model on the basis of improved principal component analysis (Improved PCA-HKNN) has been discussed in detail. Additionally, a new fault diagnosis method has been proposed. By applying the PCA improved by information entropy, the vibration and thermal original data are decomposed and classified into a finite number of characteristic parameters and factor matrices. For the peak shaving power plants, the peak load shaving state involving their methods of operation and results of vibration would be elaborated further. Combined with the data and the operation state, the HKNN model is established to carry out the fault diagnosis. Finally, the efficiency and reliability of the improved PCA-HKNN model is discussed. It’s indicated that compared with the traditional method, especially handling the large data, this model enhances the convergence speed and the anti-interference ability of the neural network, reduces the training time and diagnosis time by more than 50%, improving the reliability of the diagnosis from 76% to 97%.

scholarly journals Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4514
Author(s):  
Vincent Becker ◽  
Thilo Schwamm ◽  
Sven Urschel ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Condition Monitoring ◽  
False Alarms ◽  
Signature Analysis ◽  
Variable Speed ◽  
Monitoring Methods ◽  
Time Frequency ◽  
Motor Current ◽  
Load Point ◽  
Bearing Defects ◽  
Current Signature

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

Using CFD to Enhance the Preliminary Design of High-Pressure Steam Turbines

2013 ◽  
Author(s):  
Juri Bellucci ◽  
Federica Sazzini ◽  
Filippo Rubechini ◽  
Andrea Arnone ◽  
Lorenzo Arcangeli ◽  
...  
Keyword(s):  
High Pressure ◽  
Steam Turbine ◽  
Design Tool ◽  
Tip Clearance ◽  
Large Set ◽  
Preliminary Design ◽  
Steam Turbines ◽  
High Pressure Steam ◽  
Wide Range ◽  
Pressure Steam

This paper focuses on the use of the CFD for improving a steam turbine preliminary design tool. Three-dimensional RANS analyses were carried out in order to independently investigate the effects of profile, secondary flow and tip clearance losses, on the efficiency of two high-pressure steam turbine stages. The parametric study included geometrical features such as stagger angle, aspect ratio and radius ratio, and was conducted for a wide range of flow coefficients to cover the whole operating envelope. The results are reported in terms of stage performance curves, enthalpy loss coefficients and span-wise distribution of the blade-to-blade exit angles. A detailed discussion of these results is provided in order to highlight the different aerodynamic behavior of the two geometries. Once the analysis was concluded, the tuning of a preliminary steam turbine design tool was carried out, based on a correlative approach. Due to the lack of a large set of experimental data, the information obtained from the post-processing of the CFD computations were applied to update the current correlations, in order to improve the accuracy of the efficiency evaluation for both stages. Finally, the predictions of the tuned preliminary design tool were compared with the results of the CFD computations, in terms of stage efficiency, in a broad range of flow coefficients and in different real machine layouts.

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