System Of Thermal Power Plant Research Articles

Using Risk Points in Risk Management System of Thermal Power Plants

Using Risk Points in Risk Management System of Thermal Power Plants

Digital twin modeling and operation optimization of the steam turbine system of thermal power plants

The increasing deployment of renewable energy sources necessitates peak regulation services from thermal power plants, impacting their energy efficiency. Central to these plants, the steam turbine system significantly influences their operational efficiency. A digital twin model of this system was developed, integrating mechanism-driven and data-driven modeling methods. The neural network data-driven approach was specifically utilized for parameters such as feedwater pump speed and steam flow rate to the pump turbine. Other parameters were modeled with mechanism data hybrid driven modeling method. This model computes vital metrics such as low-pressure turbine exhaust steam enthalpy, work done and heat absorption per unit mass of steam, system efficiency, feedwater mass flow rate, and water-coal ratio—key for evaluating and enhancing the system's energy efficiency. An investigation into a reference case showed a decline in efficiency below design levels due to aging. By optimizing the live steam pressure and the cold-end system, relative improvements in energy efficiency of 0.35 % and 0.14 %, respectively, were achievable.

Performance Assessment of Single Flash PLTP LMB 1 After First Year Inspection Using ASME PTC 46:1996 Method

PLTP LMB 1 performs a First-Year Inspection after operating for one year to inspect and repair equipment after the operation. After these activities, a performance test needs to be carried out to determine the effectiveness and success of these activities. ASME PTC 46: 1996 is a widely-used standard for analyzing the performance test of thermal power plant systems, which includes plant heat rate, power output, specific mass consumption, and more. The purpose of the testing is to evaluate the results of system improvements. Calculation and comparative analysis are performed to determine the system's condition during warranty, commissioning, and testing, as well as after one year of operation and post-first year inspection. After testing, the thermal performance of PLTP LMB 1 was obtained as follows: The corrected net plant heat rate (NPRH) was 18923 kJ/kWh, the corrected net power output was 56,417 MW, and the net specific mass consumption (NSMC) was 6,881 tons/MW. Based on the data guarantee, the thermal performance value of PLTP LMB1 is still below the required value, so it can be concluded that the thermal performance value of PLTP LMB1 is in good condition. The isentropic turbine efficiency value decreased by 2.1% from its design value of 85% to 82.9%, which was caused by suboptimal heat energy absorption by the turbine above the design pressure of 5 bar.

Energy Optimization Model of Multi Energy Interaction in Thermal Power Plants with Wind Power, Photovoltaic, Hydrogen Production and Hydrogen Fuel Cell System

It is proposed that an energy optimization model of multi energy interaction in thermal power plants with wind power, photovoltaic and hydrogen production and hydrogen fuel cell system (HPHFCS). Considering that the wind power, photovoltaic and HPHFCS are connected to the auxiliary power system of thermal power plant, according to the output characteristics of the multiple power sources and auxiliary power load, an energy optimization model of multi energy interaction at the auxiliary power is proposed and analysed. This paper quantitatively analyses the role of wind power, photovoltaic and HPHFCS connected to the auxiliary power system of thermal power plants in promoting the local consumption of renewable energy sources and reducing the power consumption rate of thermal power plants. Numerical cases and simulation results validate the correctness and effectiveness of the energy optimization model in this paper.

Predictive control of SCR denitrification system in thermal power plants based on GA‐BP and PSO

AbstractAiming at the characteristics of strong non‐linearity and large inertia in the reaction process of a selective catalytic reduction (SCR) denitrification system, a predictive control algorithm based on a back propagation neural network optimized by genetic algorithm (GA‐BP) and particle swarm optimization (PSO) is proposed. First, the prediction model of a SCR denitrification system is established by GA‐BP. Second, output feedback and bias correction are used to reduce the prediction error. Third, the optimal inlet ammonia concentration is obtained by PSO. At the same time, in order to solve the problems of high dimension, large noise, and strong coupling in the original data of the SCR system in the process of establishing the prediction model, the least absolute shrinkage selection operator (LASSO) algorithm and the local outlier factor (LOF) detection algorithm are used to screen important variables and samples in the original data set of the SCR system to remove redundant variables and outliers. Finally, the simulation results show that the prediction model has good prediction accuracy and that the proposed predictive control method can achieve accurate control of ammonia injection concentration. This method improves the denitrification efficiency and reduces the NOx emission concentration, which can provide good guidance for on‐site production.

Research on optimal scheduling of thermal power system in thermal power plant based on deep reinforcement learning

Optimal dispatch is one of the key technologies to realize the efficient and economical operation of the thermal power system in thermal power plants. In order to reduce the energy consumption of thermal power system in thermal power plants, ensure the optimal dispatching effect and improve the efficiency of optimal dispatching, this paper introduces deep reinforcement learning to design a new optimal dispatching method for thermal power system in thermal power plants. The thermal power system structure of thermal power plant is analyzed, and the models of boiler, steam turbine and temperature and pressure reducer are established. The optimal scheduling problem of steam turbine and boiler thermal system is studied. By setting the objective function and determining the constraint function, the relevant optimal scheduling model is constructed. The SAC algorithm in deep reinforcement learning is used to solve the model to achieve the important goal of optimal scheduling. The experimental results show that the total fuel consumption of the proposed method is small, and the proposed method has a better optimal scheduling effect of thermal power system in thermal power plants, and can effectively improve the optimal scheduling efficiency.

Simulation and economic analysis of the high-temperature heat storage system of thermal power plants oriented to the smart grid

With the continuous increase of the grid-connected proportion of intermittent renewable energy, in order to ensure the reliability of smart grid operation, it is urgent to improve the operational flexibility of thermal power plants. Electric heat storage technology has broad prospects in terms of in-depth peak shaving of power grids, improving new energy utilization rates and improving the environment. It is an important means to promote electric energy substitution. In this study, the economics of technical application scenarios are compared and analyzed, the principle of solid heat storage technology is discussed, and its application in heating fields such as industrial steam, district heating, and deep peak regulation of congeneration units is expounded. The results indicate that in the scenario where the peak shaving subsidy and the heat storage duration are the same, as the unit output increases, the investment recovery period increases. Moreover, the results also indicate that in the 0.3 yuan/kW power market peaking subsidy scenario, only when the unit output is 0 and the heat storage time is greater than 8 h, the investment can be recovered in 5 years, while in the 0.7 yuan/kW power market peaking subsidy scenario, except for the scenario where the unit output is 40% and the heat storage time is 7 h, the investment cannot be recovered; in other scenarios, the investment can be recovered within 5 years.

Performance optimisation of the feed water system of thermal power plant using stochastic Petri nets with comparative analysis using PSO

Performance optimisation of the feed water system of thermal power plant using stochastic Petri nets with comparative analysis using PSO

Thermo-economic analysis of the integrated system of thermal power plant and liquid air energy storage

In the context of the rapid development of renewable energy, load regulation of the power grid has become a vital issue, and many researches on load regulation by thermal power plants (TPP) have been conducted. As a promising large-scale energy storage technology, the liquid air energy storage (LAES) system has significant advantages such as clean, low investment cost, and no geographical restrictions. In this paper, a coupled system combined by TPP and LAES system (TPP-LAES) is proposed, and technical and economic analysis are carried out to obtain the best configuration of the integrated system and verify its economic benefits. Technical analysis results demonstrate that the maximum round trip efficiency (RTE) of the LAES subsystem can reach 93.74 % when the extraction pressure is 242.00 bar. The RTE of the LAES subsystem of the optimal configuration is 58.31 %, at which the extraction pressure is 8.55 bar. The comprehensive efficiency of the optimal TPP-LAES integrated system is 40.86 %, which is about 1 % higher than the standalone LAES system. The energy storage density is 95.80 kWh/m3, and the net output power is 79.30 MW, exhibiting excellent system performance. The economic analysis results reveal that the initial investment cost of the TPP-LAES integrated system is 67.17 % of that of the standalone LAES system, and the levelized cost of storage (LCOS) is 154.30 USD/MWh, 24.63 % lower than that of the standalone LAES system, with outstanding economic benefits.

Application of Fieldbus Technology in Electric Control System of Thermal Power Plant

Application of Fieldbus Technology in Electric Control System of Thermal Power Plant

Brief Discussion on Energy Saving of Thermal Dynamic System in Thermal Power Plant

Brief Discussion on Energy Saving of Thermal Dynamic System in Thermal Power Plant

Analysis of the Path of Energy Conservation and Emission Reduction of Thermal Power System of Thermal Power Plant

Analysis of the Path of Energy Conservation and Emission Reduction of Thermal Power System of Thermal Power Plant

Hybrid simulation modeling framework for evaluation of Thermal Power Plants seismic resilience in terms of power generation

This study puts forward a comprehensive hybrid simulation modeling framework for assessing the resilience of Thermal Power Plants (TPPs) in terms of Nominal Power Generation Capacity (NPGC) following various earthquake Intensity Measures (IMs). The proposed framework utilizes a combination of agent-based and system dynamics simulation approaches to simulate disaggregated and aggregated aspects of thermal power plant systems with higher compatibility to investigate various earthquake physical damage scenarios and the system restoration process. A case study involving a large Gas Turbine Combined Cycle Power Plant (GTCC-PP) is examined to demonstrate the applicability of the proposed model and framework. The results of the analysis provide an estimate of the Power Plant (PP) ‘s mean NPGC vs. time and across a wide range of Peak Ground Accelerations (PGAs), with current condition (in operational status) and after employing anchoring equipment and increasing the number of repair crews as two main resilience enhancement strategies. Finally, the impact of the investigated strategies on improving seismic resilience and the seismic resilience trends in the PP under the conditions mentioned above are compared. The proposed framework provides a tool for the TPPs administrators and decision-making to plan for emergency response and evaluate the seismic resilience of TPPs and understand how to develop it efficiently.

Damage State Assessment of Metal Pipes of a Power Plant, Subjected to Thermal Stresses

During the operating of industrial equipment and installations, elastoplastic deformations may occur over time in their most demanding areas due to thermal stresses, mechanical stresses (due to internal pressure, weight, inertial forces) or thermomechanical stresses resulting from their combination. Thermal stresses are cause by the non-uniformity of the expansions produced by the temperature differences, by the non-uniformity of the coefficients of linear thermal expansion of the materials used in isothermal or non-isothermal operating conditions. Increasing the temperature reduces the material strength by creating elastoplastic deformations and thus, to the loss of available plasticity of the material. The sudden change in operating regimes and the increase in the number of these regime changes at high temperatures, is one of the main causes of the faster accumulation of degradation, and the reduction of components remaining life. On the other hand, environmental factors have an important influence on the operating behaviour of equipment, since they favour the induction of corrosion or erosion-corrosion phenomena due to the flow of fluid circulated through installations at high temperatures. The complexity of the thermomechanical stresses, to which materials are subjected during operation, leads to the need for periodic assess of their characteristics through specific tests and analyses. Paper presents the results of analyses and tests performed to assess the damage state of pipes from a thermal power plant system, results that allow to estimate the service remaining life of the pipeline system for a proper operating in safe conditions.

Design and Application of Intelligent Unmanned Spot Inspection System in Thermal Power Plant

Thermal power plant is a typical equipment-intensive enterprise, and regular inspection and spot inspection of production equipment is the most important measure to find equipment defects in time and ensure safe production of enterprises. In many years of practical application, the traditional inspection method, which relies mainly on visual inspection by spot inspectors, has been widely used. There are many shortcomings, such as missing detection, low efficiency, insufficient depth of data mining and high security risk. Based on the equipment management mode of spot inspection and regular maintenance in power plants, this paper proposes an unmanned intelligent spot inspection system for power plants, which combines intelligent sensors with diagnostic algorithms, by sorting out the work content of traditional manual spot inspection. The intelligent and unmanned management of spot inspection and regular repair in thermal power plants is realized, which has certain reference significance for improving the operation reliability of power plants and reducing the safety risk of spot inspection personnel.

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

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.

Optimization of cold-end system of thermal power plants based on entropy generation minimization

Optimization of cold-end system of thermal power plants based on entropy generation minimization

Experimental study on hydraulic performance Optimization of pump house with ultra-short forebay

The intake channel of circulating water pump house is an important part of water supply system in thermal power plant. The good hydraulic performance of intake channel is very important for operation efficiency of water pump and ensuring the safety of power plant. A hydraulic model of scale 1:12 is used to study the hydraulic characteristics of circulating pump house with an ultra-short forebay in this paper, the reasons for the formation of undesirable flow patterns, such as flow bias at the inlet, rotating filter outlet of the flow channel and backflow the circulation of the pipe at the bottom of the flapper, and the appearance of the suction vortex in the suction chamber, are analyzed, a rectifying facility consisting of "column + beam" is proposed to be installed in the forebay, a curved bias pier is added at the outlet of the traveling network, and a breast wall is suggested to be set in the suction chamber. With undesirable flow patterns in the original scheme are eliminated, the uniformity of the flow velocity distribution in the flow channel is improved, the flow pattern is relatively stable and smooth, and the ideal inflow condition of the suction mouth is guaranteed. The research results can provide a reference for the design and hydraulic performance optimization of the pump house of similar thermal power plant.

Development of a CCTV Based Smart Safety Management System in Thermal Power Plants

Development of a CCTV Based Smart Safety Management System in Thermal Power Plants

Numerical investigation on novel water distribution for natural draft wet cooling tower

Natural draft wet cooling tower is widely used in cold end system of thermal power plants, however, the water droplets in the central area cannot be sufficiently cooled due to small air flow rate. In this paper, a novel water distribution mode is proposed for the wet cooling tower, in which the water distribution area is divided into four subdomains equally in the radius direction, with the diameters of sprayed water droplets from the inside to the outside of 4 mm, 3 mm, 1 mm and 0.5 mm, respectively. The three-dimensional numerical model of natural draft wet cooling tower is developed and validated by the published data, and the cooling performance with the novel water distribution mode is then obtained. The results show that the air velocity in the central area is significantly increased thus the velocity gets more uniformly distributed with the novel water distribution mode. Besides, the thermal performance in the external area is also improved thanks to the larger contact area between the cooling air and water droplets. By comparing the proposed water distribution with the conventional one, it can be found that the outlet water temperature decreases by up to 0.69 °C, and the tower with the proposed water distribution can achieve better heat and mass transfer performances with a lower air mass flow rate, which can be recommended in engineering applications for an ideal energy efficiency of cold end system.