Biomass contains a high content of alkali metal elements, which can cause serious slagging problems during the combustion process. Coal gangue is a kind of bulk solid waste, and its resource utilization is an urgent need. In order to solve the problem of biomass combustion slagging and coal gangue utilization, coal gangue was used as an additive and mixed with sunflower straw for combustion. Physical and chemical characterization of the burned ash samples were carried out using thermogravimetric analysis (TG-DTG), inductively coupled plasma spectrometry (ICP-AES), and ash melting point tester. The effects of temperature and ratio on the combustion performance and slagging of mixed fuels were also investigated. The research results show that, during the combustion of sunflower straw, the addition of a small amount of gangue significantly increased the alkali metal content in the ash. More of these metals were converted into high-melting-point silica-aluminates, which were fixed in the ash samples, while the gaseous alkali metal content decreased. As a result, the tendency for slagging during combustion was reduced. At the same time, the sunflower straw reduced the ignition point and burnout temperature of the gangue, thereby promoting its combustion. The synergistic effect was obvious. When the proportion of coal gangue was 20%, the flammability index of the fuel reached 9.36×10^–4%/(min·℃), and the comprehensive combustion characteristics index reached 42.6×10^–7%/(min²·℃³). The combustion performance was optimal, with a softening temperature of 1 470 ℃, and the tendency for slagging was significantly reduced.

When thermal power units participate in deep peak loading, real-time acquisition of furnace temperature field is helpful to power plant boiler control and research of combustion process in the furnace. With the promotion of intelligent power generation, machine learning provides an important means for real-time acquisition of furnace temperature field. The principle and application of the three most commonly used online monitoring technologies of furnace temperature field, namely acoustic method, absorption spectral tomography and thermal radiation imaging, are summarized at first, and the advantages and disadvantages in the application of boiler furnace temperature measurement are reviewed. Then, the principle of the coupled machine learning and CFD prediction method is described in detail, indicating that the method is less affected in the harsh furnace environment, and the application research of the method in the combustion flame structure and parameters and the furnace temperature field is reviewed, demonstrating the feasibility of applying the method to the furnace temperature field, indicating it can accurately predict the furnace temperature field. Finally, the future development trend of furnace temperature field online monitoring technology and coupled machine learning and CFD prediction method is analyzed, so as to provide ideas for obtaining more accurate furnace temperature field in real time under the continuous advancement of intelligent construction of power station.

Peak regulation in thermal power plants is an inevitable trend under the development of new energy. Under this condition, the initial condensing zone of steam turbine moves forward and the corrosion of low pressure cylinder intensifies. Several methods such as electrochemical testing, sample weight loss and metal surface topography analysis (SEM, EDS, XRD, and so on) were used to study the pitting corrosion characteristics of 2Cr13 steel (the material of low pressure cylinder of the steam turbine) under the conditions of simulated initial setting zone, with different mass concentrations and different mass concentration ratios of Cl^– to SO₄^2– of three anions (Cl^–, SO₄^2– and CH₃COO^–). The test results showed that, the corrosion rate of 2Cr13 steel increased with the anions’ mass concentration, and the maximum corrosion rate (0.095 23 g/(m²·h)) occurred when was 2:1. Pitting corrosion was observed in all samples, and the number of pitting corrosion increased with the anions’ mass concentration. With the change of, Cl^– and SO₄^2– on the metal surface of 2Cr13 steel changed from site competitive adsorption effect to mutual synergistic effect, resulting in the intensification of uniform corrosion and pitting corrosion. The chloride ions and sulfate in the initial coagulation zone of steam turbine will accelerate the corrosion rate of 2Cr13 steel and the occurrence of point corrosion. In actual operation of power plant, measures should be taken to prevent the leakage of condenser tubes and the broken particles of positive resin should be effectively removed.

Solid oxide fuel cell (SOFC) is a promising energy conversion device. It has the advantages of non-pollution, high energy utilization rate, and good fuel adaptability. However, SOFC often needs to be operated under variable load conditions, which leads to the problems of shorten service life and performance degradation. The study of the relationship between the variable load characteristics of SOFC and its operating conditions is helpful to improve the output performance of the stack, extend its service life, and formulate a reasonable control strategy. A 100 W SOFC short stack testing system was developed to experimentally investigate the variable load characteristics of the SOFC stack under multiple operating conditions. The results show that, increasing the operating temperature can reduce the ohmic resistance and total polarization resistance of the fuel cell stack, thereby enhancing the stack’s steady-state output performance and dynamic response performance. Increasing the hydrogen flow rate in the medium to high current range can reduce the concentration polarization resistance, thereby effectively enhancing the stack’s peak output performance and dynamic response performance. Increasing the air flow rate has a smaller effect on the performance improvement of the stack. Under the constant flow utilization strategy, the response voltage immediately falls within the range of ±5% of the final voltage. By varying the load with constant voltage, the response current can smoothly reach a stable value. Compared with the constant flow strategy and constant current load variation method, the stack shows better dynamic response performance under the constant flow utilization strategy and constant voltage load variation method.

In order to accurately analyze the ash accumulation on photovoltaic panels, a photovoltaic dust visualization experimental platform was built, and the average grayscale value was introduced to numerically analyze the photovoltaic panel images. The clear correspondence between the average grayscale value of photovoltaic panel images and the dust density of photovoltaic panels was verified. On this basis, five fusion methods were used to fuse the visible light images and infrared images collected from the dual spectral image fusion experimental platform. The five types of fusion images were combined with visible light images and infrared images to form an image dataset. These seven types of images were identified and analyzed. The results showed that, the recognition effect of infrared images on the degree of ash accumulation on photovoltaic panels was the least affected by irradiance, with the highest accuracy, and the most significant change in the degree of ash accumulation was reflected. This conclusion can provide a theoretical basis for the study of ash accumulation rules and is of great significance for the recognition of the degree of ash accumulation on photovoltaic panels.

Superheated steam temperature is crucial for safety and economy of coal-fired power units. However, the large inertia and strong uncertainty of superheated steam temperature system make it difficult to control. To solve these difficulties, a cascade control structure based on modified active disturbance rejection control is proposed. The inner loop uses a conventional PI controller and the outer loop uses a modified active disturbance rejection controller. An engineering tuning method for modified active disturbance rejection control is provided, and a response curve to optimize the compensation time constant is designed to address the difficulty of obtaining the compensation time constant. Finally, the advantages of the proposed control strategy in tracking and disturbance rejection performance under large-scale variable loads are verified through comparative simulations and practical engineering applications. The operational data of engineering applications shows that the proposed method can ensure smaller maximum positive and negative deviations, average absolute deviation, and deviation standard deviation, which has significant advantages and potential for engineering applications.

With the rapid global industrialization and urbanization, ensuring continuous water supply and comprehensive water quality management has become a major challenge. Power plant cooling water consumption is significant, and to achieve zero discharge, many plants have adopted desalination measures to increase concentration ratios and enable water reuse. However, existing treatment technologies face high energy consumption, system complexity, and secondary pollution issues. To solve these problems, the application of electrochemical coupling pilot-scale equipment in cooling water treatment is experimentally studied, and the effects of electrochemical coupling pilot-scale equipment on scale removal, corrosion prevention and wastewater resource utilization are analyzed. The results show that, the equipment removes hardness and alkalinity significantly, and reduces conductivity and chloride ion content efficiently. Under conditions with voltage of 3.2 V and current of 240 A, the hardness removal rate reached the highest (5.15% and 55.77%, respectively), and under conditions with current of 250 A and voltage of 3.2 V, the alkalinity removal rate reached the highest (36.96% and 91.41%, respectively). The electrochemical coupling technology offers clear economic advantages compared with the conventional methods, providing an efficient, eco-friendly, and cost-effective solution for cooling water treatment with broad application prospects.

The dynamic model of lithium-ion batteries has typical nonlinearities and uncertainties, the estimation accuracy of the state of charge (SOC) of the lithium-ion batteries directly affects the effect of the monitoring and controlling in battery management system (BMS). To enhance the estimation accuracy of the SOC of the lithium-ion batteries, an adaptive sliding mode observer, which based on a variable gain for lithium-ion battery SOC estimating model is proposed. By using the robustness of the sliding mode observer and based on the second-order RC equivalent circuit model, an integral term is introduced in conventional sliding mode surface to improve the robustness on sliding mode surface, and a gradient descent rule is adopted to achieve gain adaptation to reduce the chattering of observer and improve prediction accuracy and robustness. Simultaneously, the stability of the proposed method is proved using Lyapunov theory. Finally, the proposed method is validated and compared with the sliding mode observe (SMO) method under dynamic stress test (DST) and Federal urban driving schedule (FUDS) conditions. The proposed method has less chattering in estimation with higher estimation accuracy and good robustness.

The technology of grid-forming energy storage can form a voltage source, which can support the stable operation of large power grids. The technology of grid-forming energy storage is an effective means to support the stable operation of high proportion of new energy connected to the grid. Based on this, the operation mechanism of grid-forming energy storage to support the stability of high proportion of new energy connected to the grid is analyzed. According to the principle and characteristics of grid-forming energy storage technology, five commonly used technologies to improve the stability of the grid are compared and. A model building idea of grid-forming energy storage system considering multi-time-varying parameters is proposed. Moreover, the scheme of grid-forming energy storage technology supporting high proportion of new energy grid-connected and the mechanism analysis of massive grid-forming energy storage equipment grid-connected oscillation is proposed. In addition, the system impedance dynamic identification technology based on signal injection is studied, and then the impedance reconstruction technical route of massive grid-forming energy storage equipment grid-connected system is proposed.

To address the electricity consumption challenges in remote regions beyond the reach of power grid, a novel off-grid microgrid system integrating wind and solar energy with flywheel storage technology is introduced. Research is conducted on the optimal capacity configuration of distributed power sources, and power output models are developed for wind turbines, photovoltaic arrays, energy storage flywheels, and diesel generators. With economic and reliability indicators as objective functions, and environmental protection indicators as constraints, a capacity optimization simulation for the microgrid system is conducted. By employing the improved NSGA-II algorithm, a multi-objective bi-level coordinated optimization of the microgrid system is performed, resulting in a Pareto optimal solution through multi-objective optimization. The TOPSIS method is utilized for decision-making, ultimately identifying the optimal solution tailored for the microgrid system. The data and conclusions obtained from this study have reference value for engineering applications in related fields.