In order to solve the problems of high error and low classification accuracy in the fault diagnosis process of wind turbines caused by the high dimension, feature redundancy and feature correlation of wind turbine supervisory control and data acquisition (SCADA) data, a three-stage feature selection method based on LightGBM-VIF-MIC-SFS is proposed. Firstly, based on the importance calculation of all features implemented by LightGBM, a preliminary feature space is determined. Secondly, a correlation discriminant matrix is constructed based on the variance inflation factor (VIF) and maximum information coefficient (MIC) to evaluate features with similar importance in a single screening, and discard input features with high similarity. Finally, the sequential forward search method is used to process the features for the third time, input the features obtained from the previous two feature selection one by one, and retain the features that can improve the system performance, so as to achieve the final feature selection. After the establishment of the model, the real SCADA data of the wind farm is used for performance evaluation, and the proposed algorithm is compared with the two comparison algorithms on six data sets. The results show that LightGBM-VIF-MIC-SFS has significant advantages over the two comparison feature selection algorithms. A ablation experiment was conducted on the three modules within the proposed algorithm, effectively verifying the effectiveness of each module within the proposed feature selection method and the rationality and accuracy of the optimal feature space obtained based on the proposed method.

FMEA is an effective reliability analysis method for identifying potential failure modes in a system and evaluating their criticality. Conventional FMEA has many shortcomings such as not considering the weights of different risk factors, RPN being very sensitive to changes in risk factors, difficulty in handling expert subjective scoring information, and not considering the propagation and impact relationships between failure modes. Aiming at these shortcomings, an improved FMEA method was proposed by introducing house of reliability, rough set theory and VIKOR method to improve the accuracy and objectivity of risk priority analysis of failure modes. Compared with the conventional FMEA, the improved FMEA can not only effectively deal with the various subjectivities and uncertainties in the risk assessment process but also rank the risk of the identified failure modes with taking into account the effects of failure propagation, which is able to rank the risk priority of failure modes in the case of attribute conflict and various uncertainties. Based on the proposed FMEA, a real-word application of risk priority ranking of the failure modes in wind turbine was carried out and the risk priority order of each failure mode and each subsystem were obtained. According to the ranking results, the weak links and key subsystems of the reliability of wind turbine were identified, and the hazard weight of each failure mode and subsystem was given, which can provide data support for the reliability optimization design of wind turbine.

In order to explore the effects of mixed coal combustion on reducing the carbon content of fly ash in a subcritical 600 MW natural circulation balanced draft boiler, three types of coal with significant differences in characteristics were used, and numerical simulations were combined with experiments to study the mechanism of mixed coal combustion and its relationship with fuel characteristics, burnout rate, and carbon content in fly ash in the stratified combustion process of the balance boiler. Based on the results, a set of coal blending principles are proposed to improve the combustion characteristics of coal powder in power station boilers. Firstly, three extreme operating conditions were set up, and high-quality coal was respectively focused on the upper, middle, and lower layers of the burner for combustion. And then, through computational fluid dynamics (CFD) simulations, it was found that when the coal powder airflow of the balance combustion boiler flowed upward, and the high-quality coal was distributed in the upper layer of the combustion chamber while the poor-quality coal was distributed in the lower layer, the residence time of poor-quality coal in the furnace increased significantly, allowing it to fully combust in the high-temperature area of the upper layer, resulting in a low carbon content in fly ash. Furthermore, based on the analysis of the heat value, volatile matter and ash content of coal powder in different combustion layers under various working conditions, it was found that the difference in heat value and ash content is the main factor influencing combustion characteristics. Based on the actual operating conditions of the coal-fired power plant, a more realistic set of coal blending scenarios was established, and numerical simulations and on-site experiments were conducted, which showed that the overall combustion characteristics of coal powder under each working condition followed the aforementioned rules, and the carbon content in the fly ash reduced compared with the original operating conditions. Finally, the coal blending principles were established, which take into full consideration of the actual operating conditions of the power plant, recommend high-quality coal with low ash content to be used in the upper and middle layers of the combustion chamber while poor-quality coal with high ash content should be used in the middle and lower layers of combustion burner.

Thermal power units are faced with a variety of ways to sell electric heating products, such as electricity contract market, spot market electricity sales, auxiliary service market electricity sales, civil heating and industrial heating. It is necessary to optimize the distribution of limited thermal power unit electric heating products in various markets according to the market trading mechanism, operating costs and carbon emission cost to obtain maximum profits. The intelligent decision-making system can help thermal power units to provide the optimal group and bidding strategy at all levels of the market, business accounting and the optimal operation mode of units.

An improved grey wolf optimizer (MGWO) is used to optimize BiLSTM to predict water wall temperature. The improved algorithm adopts nonlinear factor adjustment strategy, adaptive position update strategy and dynamic weight modification strategy to improve the global optimization ability of the GWO. The improved grey wolf optimizer is used to optimize the number of hidden layers, learning rate and regularization parameters of the BiLSTM model to improve the prediction accuracy of the model. The data of a power plant in Xinjiang are used for prediction simulation. The results show that, the improved optimizer has higher prediction accuracy, and can predict the change trend of wall temperature when the unit is lifting and lowering load. Compared with the LSTM and BiLSTM models, the average root mean square error of the model reduces by 9.86% and 3.69%, respectively, and the overtemperature of water wall temperature can be predicted in advance, which is of great significance for the prevention of overtemperature of water wall.

As the global climate continues to warm, capturing carbon dioxide in the air has become one of the most effective measures to reduce greenhouse gas pollution. Carbon dioxide storage systems not only store carbon dioxide in the air, but also consume excess electricity to fill the shortage of electricity supply during peak periods. As the core equipment of CO₂ storage system, the performance of the compressor directly affects the overall performance of the system. This paper summarizes the application scope and performance characteristics of seven different forms of carbon dioxide compressors and the current status of research at home and abroad. The potential problems that may exist in the application of piston, centrifugal and axial flow compressors in CO₂ energy storage systems are discussed, and corresponding suggestions and improvement ideas are given. The results of the study can provide a reference for the design and optimization of CO₂ compressors in the future.

It is necessary to break the technical barrier and make breakthroughs in information system, control system, device and other aspects when reforming large thermal power excitation system domestically. The article first systematically concludes and summarizes the key technologies of modern excitation technology, including control and command subsystems, communication subsystems, rectifier subsystems, and excitation subsystems. It analyzes and summarizes the research status, existing problems, and difficulties involved in each technology at home and abroad. Secondly, the localization and replacement process of the HN-i6200 excitation system for large-scale supercritical 670 MW thermal power units was described. Targeted design was carried out to meet the special requirements of the excitation system operation for thermal power units, and highly reliable domestic DC breakers were developed. Through experimental research, the operation strategy of the excitation system for thermal power units was overcome, and a nationally produced HN-i6200 excitation system was successfully developed. The on-site testing and system modeling have verified the working performance of the nationwide production excitation system, providing technical and practical references for the localization of excitation technology in the power industry.

Under the strategic goal of “carbon peaking and carbon neutrality”, the penetration rate of new energy sources is increasing, and coal power is gradually transforming into a flexibly adjustable power supply. While the flexible operation of coal-fired generating units will lead to energy efficiency losses and additional pollutant emissions, which have a certain degree of impact on the cost of power generation and are not conducive to the further exploitation of coal power flexibility resources. In this paper, a method for grading the flexible operating performance of coal-fired units is constructed. By calculating the performance indicators to assess the flexibility performance of coal-fired units under specific operating conditions, the relationship between the unit coal consumption rate and the generation of some air pollutants that affect the cost of power generation and the operational flexibility is investigated. Meanwhile, the kilowatt-hour cost of coal-fired power plants at the second level is estimated. Finally, second-scale operating data collected from a 330 MW unit are used to evaluate the impact of coal-fired unit flexibility on the cost of electricity generation, and build least squares support vector machine (LSSVM) model to realize the prediction of unit power cost under different flexibility levels. The research results indicate that prediction error in specific conditions can be reduced by 50% compared with that in full conditions.

In order to improve thermal efficiency of the solar cavity particle receiver, this paper designs the quartz spiral tube solar cavity particle receiver with quartz spiral tube, and establishes flow model to conduct comparative analysis on the structural parameters of the receiver. Finally, the cone angle of the cavity is set to 5°, the number of spiral turns is set to 5, and quartz window is adopted. In order to analyze the heat transfer characteristics of this receiver, this paper studied the influence of different incident radiation intensities and particle mass flow on it. The results show that, within the range of incident radiation intensity of 100 000 W/m² to 350 000 W/m² and particle mass flow of 0.002 kg/s to 0.051 kg/s, the highest particle temperature at the outlet is 672 ℃, and the highest efficiency of the receiver is 70.12%. The research has reference significance for the design of high-temperature solar particle receivers.

A regional integrated energy system based on solar, geothermal and natural gas is constructed to meet the multi-load demands of buildings, electric vehicles and hydrogen fuel vehicles. Hydrogen storage tank and heat storage tank are used to adjust the system flexibility, and to achieve systematic low-carbon economic operation on the basis of meeting the energy demand. Taking the residential community as an example, the distinctions of travel behavior for new energy vehicles on weekday and weekend are investigated, the change of travel frequency with different seasons are also considered, and the yearly loads of residential and new energy vehicles are determined. Primary energy saving rate, CO₂ emission reduction ratio and total annual expenditure reduction ratio of the proposed system are set as optimization objectives, and the capacity configuration of integrated system is optimized based on the mixed integer linear programming so as to evaluate the system performances from the aspects of economy, energy and environment. The results show that, primary energy saving rate, CO₂ emission reduction ratio, total annual expenditure reduction ratio and total investment income of the optimized system are 42.95%, 55.89%, 50.82% and 49.18%. In the integrated system, the input power of public grid only accounts for 16.93% of the total power load. This study provides theoretical basis for the integration of novel energy supply system considering coupling loads of residential building and new energy vehicles, which is helpful to promote the application of integrated energy system in building and transportation areas.