Performance enhancement in coal fired thermal power plants. Part IV: overall system

Bhatt, M. Siddhartha; Mandi, Rajashekar P.; Jothibasu, Suma; Rajkumar, N.

doi:10.1002/(sici)1099-114x(199911)23:14<1239::aid-er549>3.0.co;2-w

Cited by 18 publications

(6 citation statements)

References 2 publications

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“…The modern thermal power plants are developed to a great extent, but the essential equipment in a TPP is more or less the same, with a lot of sophistication and advancement to increase efficiencies [33]. Figure 1 shows the essential equipment in a coal-fired TPP.…”

Section: Overview Of a Coal-fired Thermal Power Plantmentioning

confidence: 99%

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

2021

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Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

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Section: Overview Of a Coal-fired Thermal Power Plantmentioning

confidence: 99%

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

2021

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“…(c) Poor coal quality: high ash coal reduce the capacity of mills, steaming rate, overloading of ID fans, ESPs, etc. [11]. (d) Poor performance of ESPs: this will overload the ID fans that cause hurdle in maintaining the furnace pressure negative.…”

Section: Plant Load Factormentioning

confidence: 99%

Control of CO2 emission through enhancing energy efficiency of auxiliary power equipment in thermal power plant

Mandi

Yaragatti

2014

International Journal of Electrical Power & Energy Systems

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“…Thus, the improvement of their efficiency is highly desirable even if only by a small percentage because of the high number of hours that this type of plants operate and to their impact on the environment. Bhatt et al studied 22 coal‐fired power plants sited in India and found net overall efficiencies ranging from 19.23% to 30.69%. They also indicated that there are considerable opportunities for applying energy conservation measures.…”

Section: Introductionmentioning

confidence: 99%

One feedwater heater taken out of service as a strategy to maintain full load and its effect on steam power cycle parameters and performance

Riesgo

Folgueras

2019

Int J Energy Res

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Summary The aim of this study is to analyze the suitability of one heater removal as a strategy for maintaining full load operation of steam power cycles when superheated and/or reheated temperatures (TSS, TRS) decrease and the effect on the net heat rate (HRNet). For this purpose, three regenerative cycles with different numbers of closed feedwater heaters were chosen. The cycles were analyzed at different steady states with Thermoflex software. Removing a heater has an important influence on the cycle operation and performance, leading to the redistribution of extraction mass flows, with the heater immediately downstream being the most affected. This may make it necessary to reduce the load of the cycle. However, when the highest pressure heater (highest PH) is removed from service, the changes are not so significant. When TSS and/or TRS decrease, the plant may not achieve full load operation. Nevertheless, if the highest PH is removed from service, it can help to recover full load. This is due to the decrease in the water/steam mass flow through the steam generator, which produces an increase in TSS and/or TRS. On the one hand, this measure leads to higher HRNet in comparison to that of the nominal conditions. On the other hand, there are certain conditions at which HRNet is lower than when all the heaters are in service and the values of TSS and/or TRS are low. Thus, for maintaining full load, the highest PH removal can be applied and cycle parameters optimized in order to reach a HRNet closer to its nominal value. The higher the number of closed feedwater heaters, the more adequate is the application of this strategy.

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Performance enhancement in coal fired thermal power plants. Part IV: overall system

Cited by 18 publications

References 2 publications

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Control of CO2 emission through enhancing energy efficiency of auxiliary power equipment in thermal power plant

One feedwater heater taken out of service as a strategy to maintain full load and its effect on steam power cycle parameters and performance

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