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.

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.

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.

Aiming at the case of large frequency fluctuation in natural gas unit black start project, combining with the gas black start system and the waveform diagram during static frequency convertor (SFC) startup, the failure phenomenon is described in detail and the specific cause of the failure is analyzed. On the basis of considering the slow response speed of diesel generator unit, a super capacitor-assisted black start frequency modulation (FM) strategy is proposed. The SFC power is divided into base power and frequency fluctuation power, among which the base power is borne by the diesel generator unit to ensure stable operation of the system, and the frequency fluctuation power is quickly responded by the supercapacitor to suppress the frequency fluctuation effectively. Through the black start experiment of a 4F gas unit, the strategy not only effectively reduces the frequency fluctuation during the black start process, but also avoids the overcurrent fault during ramp-up and load-change phase, thus the reliability and safety of black start of the gas unit is improved.

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.

In order to solve the problems of waste heat utilization, carbon capture and liquid natural gas (LNG) cold energy utilization of solid oxide batteries fueled by methane, a new type of combined cooling, heating and power system was established. The system includes the improved recompression supercritical mixed working fluid Brayton cycle, the transcritical CO₂ heat recovery Rankine cycle and the secondary mixed working fluid organic Rankine cycle. The thermodynamic analysis, equipment exergy analysis, economic cost analysis of the circulation system, and the multi-objective optimization of the system by using the genetic algorithm in MATLAB. The results show that, increasing P1, T16 and the mass fraction of R14 in the second-stage Rankine cycle can improve the network, thermal efficiency, and exergy efficiency of the system, and reduce the average unit cost. P1 is the pressure at inlet the expander in the compressed supercritical mixed working fluid Brayton cycle, T16 is the inlet temperature of the expander in the transcritical CO₂ regenerative Rankine cycle. Under the optimal working conditions, the thermal efficiency, exergy efficiency and average unit cost of the system are 64.70%, 47.85% and 24.20 dollars/GJ.

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.

In order to enhance the wind power consumption rate, achieve the goal of energy and power carbon peak and carbon neutral, with the goal of the lowest comprehensive cost of the power system, a new energy power system low carbon economic optimization method is proposed to establish the source and load side of the planning model. Firstly, on the source side of thermal power units, low-carbon transformation is performed, and a solution memory and flue gas bypass system is set up to make the unit become a liquid storage carbon capture unit, and dispatchable resources of photovoltaic power plant (concentrating solar power, CSP) and storage battery is introduced to coordinated with wind power. Then, on the load side, the price and incentive-type demand response resources are used for peak shaving and valley filling, and adjusting the users’ power consumption behaviors and power consumption. Finally, four cases are verified, and the results show that, the decarbonization of the power system from both the source and load sides can help promote wind power consumption, increase the share of renewable energy generation, enhance the low-carbon performance of the system, and save costs.

A method of identifying data by considering the excitation characteristics is proposed to solve the problem that it is difficult to select suitable samples from the historical operation data to identify the turbine work model. Firstly, Fisher’s information matrix condition number is applied to extract the excitation characteristics of the historical operating data, which together with the trend characteristics and the correlation between parameters constitute the set of feature variables. Secondly, by using the feature variables as inputs and the identification results generated based on the standard turbine work model as outputs, the Random Forest classification algorithm is used to generate a classification rule model for the identification data to realize the online selection of identification data. Finally, the accuracy of the model classification results and the identification effect of the selected data are verified. The result proves that the accuracy of the classification rule model is 97.561%, which can accurately select the sample segments containing sufficient incentives in the historical operation data, and the identification results of the turbine work model are in high consistency with that of the the standard model.

The two basic structures and working principles of open type heat source towers, as well as the technology of gas-liquid heat and mass exchange inside the tower are introduced. It systematically summarizes the structural components, antifreeze fluids, heat and mass transfer characteristics, and the current research status, with a focus on the research hotspots and major advancements in the development of antifreeze fluids and the heat and mass transfer processes of the open type heat source tower. Although the open type heat source tower technology still has disadvantages such as insufficient safety and environmental protection of antifreeze, corrosiveness and toxicity, unstable regeneration technology, and high energy consumption, it has a large heat exchange intensity, high heat recovery efficiency, simple structure, and low maintenance cost. It has broad application prospects in the field of building heating. In the future, open type heat source tower technology will develop towards a more efficient, stable, safe, environmentally friendly, energy-saving and low-carbon direction, and build a better green and low-carbon heating system.