Distributed energy power stations are developing rapidly because of their cleanliness, environmental protection, economy and high efficiency. However, there are few data used for fault diagnosis of plant equipment, so a method to predict the health state and aging degree of equipment is urgently needed. Based on this, a prediction model which can analyze the running state of equipment and obtain the deterioration trend of equipment is proposed. Firstly, multi-dimensional data of the equipment is preprocessed, and an improved Mahalanobis distance based equipment health model of distributed energy power station is constructed quantitatively by combining the analytic hierarchy process (AHP) with Gaussian mixture distribution. Then, the combined prediction model based on the improved sparrow algorithm and short and long time memory neural network is established to predict the trend and correlation analysis of the deterioration of distributed energy power plant equipment. The experimental results show that the proposed fusion health model can predict equipment anomalies in the case of insufficient actual fault data of distributed energy power stations.

A novel hybrid design that combines solid waste plasma gasification, gas turbine, absorption heat pump, and coal-fired combined heat and power plant has been proposed. In the integrated scheme, medical waste is sent to the plasma gasifier and converted to syngas, which is conveyed into the gas turbine system after the necessary treatment. In terms of waste heat utilization of syngas and flue gas, some are used by the absorption heat pump for heating, and the rest are used to heat the feedwater of the coal-fired combined heat and power plant directly. Based on a typical coal-fired combined heat and power plant, the benefits of this system are examined in terms of both thermodynamics and economics. Once the heat supply and the net electricity from coal remain the same, the net power generated by the waste in the hybrid design is 7.47 MW, while the net waste-to-energy efficiency reaches 47.96%. In just 5.23 years, the initial investment in the proposed system is recouped, and in its 25-year lifetime, the system achieves a net present value of 50 362.94 thousand CNY.

In view of the current lack of dynamic modeling and analysis of steam modeling in the integrated energy system of industrial parks, the numerical method needs to balance between accuracy and computational efficiency. Based on the basic equation of steam flow, this paper establishes a mathematical model of the hydrothermal process of steam flow. Hydrothermal model is carried out by using a mixture of analytical and numerical methods, and the thermal model is solved by finite difference method for simulation. Compared with the simulation data of commercial simulation software, the solution error of this method is about 0.43%, and the influence of different solution conditions on the thermal process is explored. It was found that an increase in spatial step size would increase the volatility of the temperature curve. As the inner diameter of the pipeline increases, the temperature transfer slows down and the response speed of the model decreases; The initial pressure does not have a significant impact on the solving process and accuracy of the model.

Coupled with the energy storage system can improve the peak shaving capacity of the thermal power unit. To improve the thermoelectric decoupling ability of the combined heat and power unit, a coupled thermal power plant combined heat and power unit with liquid carbon dioxide energy storage system is proposed. The system utilizes the condensate to recover the compression heat of the carbon dioxide during the charge process, and supplies heat to the users together with the heating extraction steam. Besides, the heating extraction steam is employed to preheat the carbon dioxide of the expander inlet during the discharge process. Based on the established thermodynamic models, the thermal performance analysis of the coupled system was carried out with the thermal efficiency, exergy efficiency, and electricity storage efficiency as assessment criteria. The sensitivity analysis results indicate that increasing both the expander inlet temperature and the discharge pressure can obtain a higher system exergy efficiency and electricity storage efficiency; increasing the charge pressure results in a higher system thermal efficiency, while the exergy efficiency first increases and then decreases. The parameter optimization of the corresponding CO₂ energy storage system was carried out under the design parameters. Results show that when the charge pressure is 10.5 MPa and the discharge pressure is 18.0 MPa, the coupled system achieves the optimal efficiency of 64.92%.

There is a common mismatch between heating supply and demand parameters for industrial heating retrofits of pure condensing thermal power units, and the benefits of thermal power plants can be improved by adopting a reasonable matching scheme of supply and demand parameter. Aiming at the phenomenon of energy mismatch caused by the excessively high extraction parameters of the unit in the industrial heating scene, considering that it is suitable for high-parameter industrial heating, this paper proposes a scheme of using the centripetal turbines for cascade utilization of extraction steam. Taking the industrial heating transformation of a domestic 330 MW cogeneration unit as an example, the thermal system model was constructed to comprehensively evaluate the performance indicators changes of the system from three aspects: thermal performance, exergy environment and economic performance. We also comprehensively compared the effect of upgrading the direct heat reduction and pressure reduction method to the radial turbine power generation steam energy cascade utilization scheme. The calculation results show that under the same heating parameters, compared with the traditional method of temperature reduction and pressure reduction, the comprehensive benefits of the industrial heating steam energy cascade utilization scheme based on centripetal turbine power generation is significant. The specific performance is that under rated conditions, the gross coal consumption rate for power generation can be reduced by 0.89 g/(kW·h), the extraction exergy efficiency can reach 97.66%, and the direct economic benefit is relatively increased by 1.04%, and the carbon transaction cost relative reduction of 0.34%. In addition, the relative advantages of all aspects performance of the system will expand with the increase of the amount of steam extracted by the system for industrial heating.

The penetration rate of distributed photovoltaic power stations in the power system is increasing year by year, to ensure the safe and stable operation of the power grid, a distributed photovoltaic ultra-short-term power prediction method based on combined neural networks is proposed. Firstly, a 1DCNN&1DCNN-LSTM combined neural network model is constructed by using 1D convolutional neural network (1DCNN) and long short-term memory (LSTM) neural networks, to obtain multi location numerical weather prediction (NWP) information and historical power information, using combined neural network model for spatially correlated photovoltaic power prediction and time series prediction; and a fully connected neural network (FCNN) is added to the combined neural network model, which is used to learn and assign weights to the two prediction results, achieving ultra-short-term prediction of distributed photovoltaic power generation. The validation was conducted using measured data from a photovoltaic power station in Hebei, and the results showed that this method can effectively improve the accuracy of distributed photovoltaic prediction and has certain practical value.

The combustion of biomass will not increase carbon dioxide emissions, and the development of biomass coal coupled power generation technology is a feasible way to accelerate the transformation and upgrading of electricity. The compressed biomass and coal powder are ground in different coal mills without mixing, and are fed into the furnace by different burners. This method does not require additional biomass treatment equipment or setting up a biomass fuel treatment workshop, which can greatly save investment and factory space. The key limiting technical factor is whether the pulverizer of the power plant can effectively grind biomass Pellet fuel, and how to evaluate the adaptability of the existing pulverizer to the new coupling fuel. To this end, a medium speed coal mill experimental platform was built to analyze the adaptability of the medium speed coal mill in grinding biomass particles. The results show that the grinding output of the medium speed coal mill decreases when grinding wood chip biomass particles, and the effective output correction coefficient is 0.65~0.70; When the ZGM medium speed coal mill grinds sawdust particles, the loading force characteristics and separator characteristics both vary significantly at the particle size boundary of 0.2 mm. Therefore, the screening fineness R₂₀₀ and R₅₀₀ can be considered as the measurement standards for the uniformity of biomass particle materials ground by the coal mill; Compared with pulverized coal, the increase of loading force of milled biomass particles or the increase of rotating speed of the current separator has a large difference in the influence of fineness distribution, which needs further in-depth research.

Condensate downcomer vibration is a common phenomenon in the direct air cooling system, which endangers the safe operation of the air cooling system when the vibration is serious. The pipe vibration model is established based on the fluid solid coupling method. The two-phase flow characteristics of the condensate pipe are analyzed using the theory of flow induced vibration and fluid cavitation. The model analytical calculation and numerical simulation are carried out for the condensate pipe of a 660 MW ultra supercritical unit. The results show that saturated or nearly saturated condensate flows from the air cooling platform along the condensate downcomer to the hot well of the steam exhaust device by gravity flow. When the potential energy of water is released, two-phase flow excitation and cavitation phenomena occur. Based on this analysis, the causes of condensate vibration and cavitation noise are found, By using multi-stage Venturi tubes and porous orifice plates in the condensation water pipeline of the power plant to eliminate the condensate potential energy, avoid excessive pipeline vibration and noise, and eliminate the hidden danger of pipeline vibration.

The overheating of boiler heating surface seriously affects the safe operation of the power plant. It is of great significance for the safety of boiler to predict the tube temperature of heating surface and to take appropriate preventative measures. A data driven-based model for tube temperature prediction is proposed in this study. Firstly, the key variables affecting the tube temperature are selected by grey correlation analysis that affect the wall temperature of the heating surface, and a wall temperature prediction model based on long short term memory (LSTM) neural network is constructed. Then, the correlation feature coefficients under similar historical operating conditions are defined, and the predicted wall temperature obtained by the LSTM neural network is corrected to improve the model's prediction accuracy. Finally, an on-duty supercritical boiler with 600 MW capacity is taken as the case study. Results showed that the relative error of the proposed prediction model is within (−2.5%, 2.5%). The average relative error is 0.40%, and the average tube temperature prediction error is 2.24 ℃. It indicates that the proposed model is helpful for the tube temperature prediction of the boiler under complex operating conditions.

Conventional industrial steam turbines are usually equipped with quick-closing inlet valves to realize emergency shutdown of the turbine. In this paper, a negative pressure inlet condensing steam turbine was studied has low inlet parameters, large inlet steam volume flow and small pressure difference between inlet and exhaust steam ends. In order to reduce the intake pressure loss, the quick-closing inlet valves are not employed, but a redundant large-diameter rapid vacuum breaker valve is used at the exhaust end of the turbine. When the turbine needs emergency shutdown, open the quick vacuum breaker valve, so that the atmosphere quickly enters the exhaust end of the turbine, the pressure of the exhaust end of the turbine rapidly rises, In this paper, the steam turbine whose inlet and exhaust steam pressure difference is 40 kPa is studied, and the pressure difference between the inlet end and the exhaust end of the turbine could reach 0 within 4 seconds, so that the turbine is closed, and the purpose of emergency shutdown of the turbine is achieved.