In order to study the unstable characteristics of vapor-liquid two-phase flow in water-cooled wall pipe of an opposed combustion natural circulation boiler during deep peak shaving, a frequency domain mathematical model suitable for different working conditions is established. By performing small perturbation linearization on the mass, energy, and momentum equations, eliminating high-order infinitesimal perturbations and steady-state quantities, and conducting Laplace transformation, the transfer function used to describe stability of the vapor-liquid fluid flow in the pipeline is obtained through the Nyquist diagram. The graphical method is used to judge the stability of the working fluid flow in the pipe. The calculation results show that, the critical heat flux densities of typical circuits operating at 25% BMCR and 50% BMCR are 182.20 kW/m² and 240.13 kW/m². Moreover, this model is used to calculate the unstable boundary of the water wall pipe section of a 350 MW natural circulation boiler and study the influence of parameters such as inlet subcooling, mass flow rate, pipe length, inclination angle and inlet throttling coefficient on the flow instability characteristics. The calculation results show that, the influence of inlet subcooling on critical heat flux density is non-unique, and the unstable boundary diagram shows a “double-C” shape. Increasing the mass flow rate reduces the density difference between the inlet and outlet of the fluid, which is beneficial to the flow stability. Increasing the heat flow density increases the density difference between the inlet and outlet of the fluid, which is not conducive to the stability of the flow. Enhancing the inlet throttling coefficient can suppress the pulsation of the flow at the inlet, which is conducive to the stability of the flow. Increasing the inclination angle of the pipe will increase the weight pressure drop and increase the disturbance caused by it, which is not conducive to the stability of the flow.

The effect of chloride ion mass concentration (0, 1, 20 mg/L) on stress corrosion cracking of Inconel 740H, a candidate nickel based alloy for high parameter ultra-supercritical units at 630~700 ℃, were investigated by slow strain rate tensile testing and stress corrosion crack propagation testing. Moreover, the relevant mechanism of high mass concentration chloride ions promoting the initiation and propagation of stress corrosion cracks was explored. The results showed that, high mass concentration of chloride ions promoted the stress corrosion cracking of 740H alloy, and the stress corrosion sensitivity index I_scc(δ) of the alloy increased with the chloride ion mass concentration. When the mass concentration of chloride ions increased to 20 mg/L, both the middle and edge of the fracture exhibited intergranular brittle fracture characteristics, with a large number of secondary intergranular cracks near the fracture. At this point, the alloy experienced stress corrosion cracking. The average crack propagation rate in a 20 mg/L chloride ion water environment reached 1.15×10^–6 mm/s, which is 111.7 times the average crack propagation rate in high-purity water.

In cooling systems of thermal and nuclear power generating unit, the bearing fault signal of the motor is weak and nonlinear, which is easily masked by running signals and invalid signals, and the use of a single vibration monitoring may not be sufficient to collect complete defect information. To address this problem, vibration and sound signals are combined to monitor bearing fault signals, and the collected sound and vibration signal features are fused. To process the sound and vibration signals of motor bearings, a WR-VMD algorithm that integrates wavelet ridge (WR) and varational mode decomposition (VMD) is proposed. The WR is used to analyze the components of the original signal, and then the acquired information is used to determine the parameters of the VMD, which makes up for the shortcomings of the original VMD method that requires the parameters to be set empirically in advance. The simulated signal results show that, compared with the same type of methods, the features extracted by the WR-VMD method are the most obvious and have the least interference information. Finally, the acoustic and vibration signal fusion technique and the WR-VMD algorithm are applied to the measured motor bearing fault data, and the results show that, compared with other feature extraction algorithms of the same type, the WR-VMD extracts the most obvious features and has the highest accuracy in fault diagnosis. The acoustic and vibration signal fusion has at least a 7% increase in accuracy compared with a single vibration or acoustic signal in fault diagnosis.

The fuel management system of power plants is the core of coal management in thermal power plants, of which domestic substitution is a necessary path for the power plant informatization, and the localization of databases is the key. Data migration plays an important role of the handover between the old system and the new one. An optimized data migration scheme is proposed based on a thorough study on data migration methods and the data characteristics of fuel management system in power plants. Focusing on the test study of methods for verifying data consistency, the characteristics of sequential comparison and dichotomy are analyzed, and it is proved that dichotomy has certain performance advantages when the proportion of corrupt data is relatively small and the distribution is concentrated. Enlarging the proportion and decentralizing the distribution, dichotomy costs more time obviously. A data migration tool for plant fuel management system is designed and implemented, which realizes visualization of data verification. The tool supports batch operations for functions like configuring verification rules, setting execution methods and providing feedback on results. It also enriches verification rules, supports customized rules and adds the mechanism for exceptions and so on. Divided into user layer, service layer and data layer, this tool achieves data migration of plant fuel management system.

Under the premise that deep peaking of thermal power units has become normal operation, it poses a higher challenge to transformation of industrial steam supply of thermal power units. Three steam supply schemes using reheater recirculation cooling as the core technology are proposed to meet the requirements of high-pressure steam supply transformation of 660 MW supercritical units. Moreover, the feasibility and economy of these schemes are analyzed by thermodynamic calculation under varying working conditions. The calculation results show that, all the three schemes can ensure the safe operation of the reheater under non-overtemperature conditions, and greatly improve the wide load high pressure steam supply capacity of the unit at 30% rated power load or above and under conditions that meet the demand of single unit with 200 t/h, 6.0 MPa and 480 ℃ steam supply. In order to avoid overspeed of the flow rate at the reheater outlet, it is necessary to coordinate the operation of the immediate pressure (IP) control valve to reduce the flow rate of the reheated steam by increasing the pressure of the reheated steam. With the decrease of the load, the reheater recirculation flow rate under the rated steam supply flow rate will increase. The recirculation flow rate under the whole working conditions of scheme 1 and scheme 3 is not much different, and the ratio of the recirculation flow rate under the high and low load of scheme 2 can reach more than 5 times. Among the three steam supply transformation schemes, scheme 2 is the most energy efficient, scheme 3 is second, and the three schemes can produce economic benefits of 39.51, 44.45 and 41.78 million yuan each year, but in the implementation process, the selection of schemes should consider factors such as investment cost, operation and maintenance amount and energy saving income.

Reliability model is the foundation of reliability analysis. Conventional reliability modeling takes the entire system as the research object, and uses all fault data to fit the distribution function of the system, estimate parameters, and optimize the model, thus to determine the distribution type and distribution function of the system, and then to calculate the reliability indicators of the system. Wind turbine is a typical complex electromechanical system, with different functions, structures, and fault forms of each subsystem. It is obviously inappropriate to use one distribution function to determine the fault distribution of the entire system. Therefore, based on the collected and sorted fault data of wind turbines, the distribution function optimization-based reliability modeling and analysis technology of wind turbines is proposed. By applying the commonly used exponential distribution, normal distribution, log-normal distribution, Weibull distribution and gamma distribution, the distribution function fitting, parameter estimation and goodness of fit analysis of the fault interval of each subsystem of wind turbines are carried out, and the distribution function and subsystem reliability function of the fault interval time of each subsystem are determined. On this basis, a Copula connection function is used to establish the reliability function model for wind turbines, taking into account the fault correlation between subsystems. Moreover, an example analysis is conducted on the fault data of an offshore wind turbine, which verifies the feasibility of the proposed method.

Against the dynamic operation characteristics of three-pressure reheat waste heat recovery steam generator (HRSG) of gas turbine combined cycle (GTCC) unit, the Modelica open-source programming language is used to build the simulation model for the HRSG, and the simulation results are compared with the actual operation data during peak load regulation process of the power plant. Under actual operating conditions, the established model can accurately predict the dynamic response laws of main parameters of the HRSG in the dynamic process of steady-state operation after the load drops from full load to low load, then increases from low load to full load, as well as unit shutdown and boundary parameter disturbance. When the unit load decreases from 320 MW to 280 MW, the power of the steam turbine decreases from 124.0 MW to 111.5 MW. The high pressure main steam flow rate decreases from 70.12 kg/s to 63.62 kg/s, and the high pressure main steam pressure decreases from 8 250 kPa to 7 612 kPa. The flue gas parameters at inlet of the HRSG change in about 300 s with the decrease of the unit load, while the steam parameters of the HRSG need about 600 s to complete dynamic response and reach steady state at low load, indicating there is a certain lag in steam parameters of the HRSG compared to the change of gas turbine exhaust gas parameters over time. In the dynamic process the unit shut down, the power of the steam turbine decreases from 130.4 MW to 5.4 MW, the high pressure main steam temperature of the HRSG decreases from 600.1 ℃ to 224.5 ℃, and the high pressure main steam flow rate reduces from 76.1 kg/s to 15.3 kg/s.

Compared with magnetic particle detection and ray detection, magnetic memory detection is more sensitive. The magnetic memory technology is employed to carry out nondestructive testing and evaluation for weld defects of 20 steel plate. The results show that, the magnetic memory detection method can effectively characterize the weld defects of steel plates. Compared with the magnetic field intensity parameter, the magnetic field gradient value is more sensitive to the weld defects of the four tested steel plates, which manifested as its normal component increases significantly and presents a convex peak, and the abnormal magnetic memory signals also appear in some places of the weld with local stress concentration. In practical application, the rapid characteristics of the magnetic memory detection technology can be used to extract the abnormal signals of the weld seam of the tested components at first, and then compare it with other non-destructive testing methods, to effectively improve the accuracy and reliability of defect detection.

To solve the problems of slow milling response and poor control precision occurred in direct-blow pulverizing systems, a novel pulverizing system adding small pulverized coal silos is designed, and the corresponding air-powder control strategy is proposed. Moreover, based on the dynamic simulation model of an ultra-supercritical unit, the operation and control strategy of the novel pulverizing system is simulated and validated. The results demonstrate that, the proposed control strategy enables precise and rapid response of air-powder parameters. In the load response simulation tests conducted within the load variation range of 75%~85% of rated load, after the small pulverized coal silos were put into operation, the load response rate of the unit’s model can reach 5%/min and recover stability faster. Therefore, the novel pulverizing system and its operational control strategy significantly enhance the unit flexibility. The research provides a viable technical approach and guidance for improving the flexibility of coal-fired boilers.

This paper focuses on aggregating decentralized demand-side resources through virtual power plants (VPPs) to enhance the peak load management capability in the construction of new electric power systems. The research centers on the core issue of “load-based” virtual power plants participating in power source planning. It investigates the modeling methods for the adjustable potential of virtual power plants, including electric vehicles, air conditioning loads, and industrial loads. Moreover, the paper delineates the economic and operational technical constraints of virtual power plants in power source planning and constructs a power source planning model that accounts for VPPs. Considering the prediction errors of renewable energy and the characterization errors of the VPPs’ response potential, the paper utilizes interval optimization theory to reformulate the model into an interval planning model and completes the deterministic transformation of the model through interval order relations and interval possibility. The rationality of the proposed power source planning method is verified through a case study. The results show that the proposed method can effectively aggregate VPP resources, significantly enhancing the system’s peak load management capability. The constructed interval planning model can effectively handle forecast errors, ensuring the reliability of planning. This method provides an economically feasible solution for the transformation of the source-load structure in new electric power systems. It is evident that the aggregation and optimized dispatch of VPPs can significantly enhance the flexibility and reliability of the power system.