The dynamic characteristics of the bottoming cycle of a gas turbine combined cycle have a significant influence on the load variation characteristics of the unit. Partially recuperation is a new method which can be used to improve the performance of combined cycle at partial load, it is an important part of system feasibility evaluation to study the effect of partially recuperation on the dynamic characteristics of the bottoming cycle. In this paper, a dynamic simulation model of the bottoming cycle system of a partially recuperative combined cycle unit is established by using modular modeling method, and the dynamic characteristics of inlet parameter disturbance and load shedding process are studied. The results show that, the dynamic model can accurately reflect the dynamic characteristics of the bottoming cycle, and the simulation results show that the dynamic response of partially recuperative units facing the disturbance of exhaust parameters is consistent with that of conventional units. The disturbance of exhaust temperature T₄ mainly affects the high-pressure superheated steam and reheated steam, and the influence range is larger. The disturbance of T₄ with 5% can reduce the bottoming cycle power by 16.32%. The response speed of the unit is slower, and the time constant of the steam turbine power is about 400. The disturbance of exhaust flow affects the steam of each stage, and the influence range is relatively small, the disturbance of 10% reduces the bottoming cycle power by 9.49%, the response speed of the unit is faster, the time constant of the steam turbine power is about 60. When the recuperative ratio is disturbed, the dynamic response of the unit is similar to that of the T₄ disturbance, and the operation strategy of recuperative regulation results in the load variation of the combined cycle being borne entirely by the bottoming cycle, the time needed for partially recuperative units to reach steady state is 1 100 s later than that of conventional units, and the recuperative regulation mode is suitable for use in the load interval below 51.4%.

For the purpose of absorbing renewable energy, the coal-fired power units are required to operate flexibly, and the resulting variable operating environment will lead to large nonlinearity and uncertainties of the wet desulphurization process. Particularly, the time delay will make the control even harder. Therefore, in order to achieve a more flexible control structure, a new desulphurization control strategy based on frequency retrofit is proposed. Based on the strategy, a dynamic model is obtained through field experiments. Meanwhile, in order to deal with uncertainty well and achieve safe compensation of uncertainty in the control process, an updated Gaussian process model predictive control method for time-delayed objects is proposed, and the performance of the method is demonstrated by parameter analysis and simulation experiments. Finally, the effectiveness of the proposed control strategy and control method is verified by field application.

To establish an accurate and effective dynamic model of cogeneration units, a modeling method based on digital twin technology is proposed using unit operation data. Firstly, the historical data stored in the unit data server is extracted, it is then clustered using the improved genetic simulated annealing fuzzy C-means method to establish a historical data clustering library. Then, during the operation of the unit, real-time operational data is collected and transmitted, and a multi-level similarity recognition strategy is used to retrieve the historical data closest to real-time operational data in the historical data clustering library. Then, based on the optimization, the extreme learning machine will use the searched historical data for unit modeling. Finally, a twin model of a cogeneration unit in Hangzhou is established and comparative experiments are conducted. The results show that, the built model meets the accuracy requirements and can track the real-time state response of the unit. The model accuracy can be further optimized by flexibly changing the parameter settings during the modeling process.

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.

In response to the instability problem of distributed new energy as an independent power source supplying energy to household energy systems, a capacity configuration method for distributed home energy systems based on hydrogen energy is proposed. This method is based on the characteristics of hydrogen energy that can suppress wind and solar fluctuations and flexibly convert load demands, and constructs a hydrogen coupled distributed energy family terminal energy system structure. Combining with the characteristics of renewable resources in the region, a hydrogen based distributed household energy system capacity allocation model is established with the goal of minimizing the net total cost. Taking the wind and solar resources and typical household energy data in Xinjiang region as an example, the impact of the configuration capacity of distributed energy and hydrogen energy systems on the system is simulated and analyzed, and the optimal capacity configuration of hydrogen energy systems under the optimal wind and solar ratio conditions is obtained. From the simulation results, it can be seen that the proposed model can effectively reduce the total cost of the household energy system while achieving reliable energy supply off the grid, and promote the consumption of new energy, which provides suggestions for the design of distributed household energy based on hydrogen energy.

A Python model was established for thermal performance of the heavy-duty gas-steam combined cycle triple-pressure heat recovery steam generator. The model calculates the detailed heat recovery steam generator parameters under the condition of changing unit load, including main steam pressure and flow rate, heat and heat transfer coefficient of each heat exchanger, as well as power output and efficiency. As the effect of exhaust gas temperature and flow rate on the heat recovery steam generator is analyzed, how the ambient temperature, humidity and fuel heating affect the heat recovery steam generator output is also discussed when the unit is in part-load. It is verified that the model has good simulation accuracy and calculation efficiency. Simulations for one certain frame gas turbine combined cycle show that: When the unit load is reduced from full load 650 MW to partial load 250 MW, the triple main steam pressure and feed water flow rate of heat recovery steam generator decrease, the steam turbine power output decreases from 219.1 MW to 130.4 MW, and the efficiency of heat recovery steam generator increases from 89.3% to 92.1%; main-steam flow increases as the flue gas flow rate and temperature increase at the inlet of heat recovery steam generator; As the load of the random group decreases, the heat transfer coefficient and heat transfer amount of each heat exchange surface of the waste heat boiler decreases, but the proportion of heat exchanger between the flue gas and the high-temperature section heat exchanger in the total heat increases, and the heat transfer between the flue gas and the low-temperature section increases. The proportion of heat exchanger heat transfer in the total heat is reduced; when the unit load is reduced from 650 MW to 300 MW, the proportion of steam turbine shaft power increases by 1.67 percentage points, the proportion of heat loss in the chimney flue gas decreases by 2.63 percentage points.

Under partial shading conditions (PSC), the P-U characteristics of a solar photovoltaic array may exhibit multi-peak phenomena. Conventional algorithms tend to fall into local maximum power point (LMPP), while maximum power point tracking (MPPT) methods based on meta heuristic algorithms are difficult to balance speed and accuracy. In this regard, this paper designs a hybrid algorithm based on the improved particle swarm optimization (IPSO) with embedded the improved perturbation and observation (IP&O). The velocity and position of the particle are first updated by the IPSO algorithm. Then, perform MPPT based on the position of particles using the IP&O algorithm. The tracked power is used as the fitness value of the particles, so that IPSO can find the global maximum power point (GMPP) among many LMPPs. Finally, with the global optimal output of IPSO as the initial position, IP&O is used again for global maximum power point tracking (GMPPT). Comparing the proposed algorithm with IP&O, IPSO, and IPSO-P&O through simulation, the simulation results show that the proposed algorithm performs excellently in tracking speed and accuracy, especially in the case of a wide voltage search range, and has smaller power oscillations during the tracking process.

To solve the problem of excessive air leakage from the four-compartment air preheater, a four-compartment rotary air preheater of a 1 000 MW power plant is used as the study object to investigate the thermal characteristics, rotor thermal deformation and air leakage of the air preheater, by combining theoretical analysis with numerical simulation. The numerical simulation results show that, for every 1% increase in air leakage rate of the 1 000 MW unit, the heat transfer efficiency of air preheater will be reduced by 1.13%, and the power plant will consume 3 604 tons of standard coal more on average per year. There are large temperature gradients in the axial and circumferential directions inside the air preheater, and most of the corrosion and deposition areas are distributed in the low temperature section. There are large differences in rotor thermal deformation during different operating conditions of the air preheater, and the maximum thermal deformation occurs on the secondary air side. It is necessary to make corresponding adjustments according to the thermal deformation characteristics of different positions of the rotor when setting the sealing system.

In order to explore the formation and evolution of soot during the combustion of coal, this paper uses Pingdingshan coal as combustion material, and uses the light scattering method coupled with the thermophoretic sampling article diagnostic method to measure the soot mass in flame. A light scattering measurement system capable of precise vertical movement is constructed to measure the light scattering intensity at different heights of the flame. The particle size distribution is obtained by the thermophoretic sampling particle diagnostic method, and the soot mass at different heights when the flame is calculated by Mie scattering theory. The results show that as the flame rises, the median mass diameter of soot firstly increases and then decreases. When the flame burns stably, a large amount of soot is formed at a height of H=10 mm. As the flame rises, the mass of soot decreases rapidly in the H=10~30 mm range. In the range of 10~20 mm, the soot mass of Pingdingshan coal decreases by 58.62%. When H>40 mm, the soot mass of Pingdingshan coal slowly decreases.

The typical faults of wind turbines are summarized. The fault data and non-fault data of converter system, generator system, variable propeller system and auxiliary power system with high fault frequency of wind turbines in a wind farm are selected for fault diagnosis research. The fault diagnosis model is established by ELM, SVM, KELM and WOA-KELM algorithms respectively. At the same time, Laplacian scores are used to sort and select the importance degree of model characteristic variables. WOA-KELM algorithm achieves better diagnostic effect by optimizing the regularization parameter C and kernel parameter γof KELM algorithm. The results show that, the diagnostic accuracy of the four algorithms for non-fault types is 100% under different sample numbers. The average diagnostic accuracy of WOA-KELM algorithm improves from 88.0% to 93.2% after feature screening by using Laplace scores. In the range of 250~500 samples, the diagnostic accuracy of WOA-KELM algorithm reaches the maximum of 96.0% after feature screening. It is proved that this model can effectively realize the fault diagnosis of wind turbine, and provide guidance and reference for field operation and maintenance personnel.