Nowadays, the third energy revolution has taken place. Many developed countries have formulated clean energy development strategies and announced the time for phasing out thermal and nuclear power to reduce carbon emissions. Meanwhile, China has made a commitment to the world that the carbon emissions of China will peak before 2030, and the carbon neutrality will be achieved before 2060. Therefore, it is of great significance to study the development pathway of clean electricity of China. The reserves and characteristics of clean energy such as hydro, wind, and solar in China are analyzed. The medium and long-term power demand of China is projected, and the power system structure in 2030 and 2050 are respectively estimated based on the electric power and energy balance equations. In addition, the trend of carbon emissions is also analyzed. Some suggestions are proposed to guide the development of China’s clean electricity. The results indicate that the “carbon peaking” of China’s power system would arrive in 2027, and the clean electricity of China is projected to exceed 50% of the total energy production in 2030. Thermal and nuclear power can be replaced by clean electricity such as hydro, wind, and solar energy in 2050, the power industry will achieve “zero CO₂ emission”, and the transformation of the green power system will be achieved in response to carbon peaking and carbon neutrality goals.

To improve the utilization rate of clean energy, reduce carbon emissions, and alleviate the global energy crisis and greenhouse effect, a low-carbon economic dispatch model of multi-energy park considering high proportion of new energy consumption is proposed. First, after introducing the gas storage and heat storage equipment to the park, the potential of energy coupled devices is further tapped, and the impact of electric vehicle charging mode is explored. Then, based on the stepwise price curve, a price-based integrated thermo-electric demand response model is established. Moreover, considering the low-carbon operation of the integrated energy system, a carbon capture and storage equipment model is built. Furthermore, a mixed integer linear programming model for low-carbon economic dispatch before the day of the multi-energy park is proposed. The example analysis shows that the proposed model can improve the energy utilization rate and the scheduling flexibility of the park, effectively reduce the carbon emissions of the park, increase the income of the park, and promote the consumption of high proportion of new energy.

The large-scale utilization of renewable energy is an important way to achieve the “double carbon targets”. The technology of coupled renewable energy with hydrogen system can improve the consumption rate of renewable energy and the penetration of new energy vehicles. The coupling between the electricity-hydrogen energy system and the transportation system will be even closer in the future. Based on the access of large-scale new energy vehicles, first, the development of the electricity and hydrogen energy system was summarized, and the three working modes of electricity-hydrogen coupling system including hydrogen production, output smoothing, and coordinated operation with electricity network were introduced. Then, the research status of the electricity-transportation coupling system on planning and optimal operation, and the problems of hydrogen-transportation coupling system on hydrogen refueling station optimization and hydrogen transportation were analyzed. Finally, in combination with the existing bottlenecks, the future feasible research directions such as dynamic model construction and the influence of uncertain factors were proposed.

The access of a high proportion of renewable energy has posed new challenges to the supply reliability of the power system. The system must have sufficient capacity credit to cope with the output fluctuation and randomness of renewable energy. Due to the nonlinear relationship between energy storage capacity credit and power planning results, it is difficult to establish accurate capacity adequacy constraints for traditional power planning methods. Therefore, a generation expansion model is established, in which thermal power, renewable energy, energy storage, and demand response resources are incorporated, with the full-year hourly production simulation to ensure adequate operation flexibility and improved capacity adequacy constraint to incorporate the capacity value of energy storage and demand response resources. An iterative algorithm is designed to solve the nonlinear problem of energy storage capacity credit, and the validity of the model is verified by some regional grid in China. The results show that in the high-proportion renewable energy system, the system capacity is surplus, and the main factor affecting the system cost is the flexibility constraint. The introduction of a small amount of demand response resources can greatly reduce the system cost, which provides new ideas for power system planning at a high proportion of renewable energy.

The development of renewable energy represented by wind, photovoltaic, and hydropower can increase the uncertainty of power systems. In order to ensure the flexible operation of power systems with a high proportion of renewable energy, a power system flexibility evaluation method based on typical operating scenarios was proposed. Through a modified K-means algorithm, the operating scenarios of renewable energy and load were clustered to obtain typical scenarios. The flexibility evaluation indexes were proposed from three perspectives including regional supply and demand balance, regional power flow distribution, and inter-regional transmission capacity. The flexibility evaluation index of each scenario, and the comprehensive evaluation index based on the appearance probability of each scenario were calculated to evaluate the flexibility of the system. Based on the actual historical output data of new energy and the load of a certain region in the south, the flexibility evaluation was performed on a modified IEEE 39 system. The results show that the proposed clustering method and flexibility index can effectively reflect the flexibility of the system.

Plain weave SiC/SiC composites were manufactured by the chemical vapor infiltration process，and X-ray computed tomography non-destructive testing technology was used to investigate the damage evolution and failure mechanism of textile ceramic matrix composites. Besides the third generation SiC fiber toughened plain weave laminated SiC/SiC dog bone test pieces were prepared. In addition, a CT in-situ tensile tester was developped, the nano in-situ X-ray CT tensile tests were completed, and the three-dimensional reconstruction images and scanning electron microscope photos of CT scans were analyzed. The results show that nano X-ray CT in-situ tests can reveal the evolution of tensile damage of materials. The uniaxial tensile stress-strain curve of plain weave SiC/SiC composites exhibits obvious nonlinear characteristics, with damage initiating in the stage of nonlinear changes. First, lateral cracking of the substrate occurs and gradually expands with increasing tensile force. Next, interlayer matrix cracking and fiber bundle matrix cracking occur longitudinally and gradually expand to the fiber bundle width. Finally, the fiber in the tensile direction breaks and the material fails. Most of the substrates have transverse cracks closed, but the longitudinal fiber bundles and the matrix between the bundles are severely separated, the fractures are uneven, and there are obvious fiber pull-outs.

An integrated port energy system planning model is established considering the flexibility of shore power load to finely model the shore power load. Next, the proposed model is decoupled into shore power load elasticity and integrated energy system planning. Then, the shore power load curve of the port-ship master-slave game model is calculated. Finally, the optimal response method is used to iteratively solve the optimal integrated energy system planning method considering the shore power load elasticity. The simulation results show that the model can help rationally allocate resources in the port area, effectively improve the energy efficiency of the port area, increase the revenue of port area, and help the port area to save energy and reduce emissions.

A decision model of opportunistic maintenance based on dual time window strategy is proposed for the multi-unit serial system. Instead of taking the arrival of a single unit’s optimal maintenance interval as the system shutdown condition in traditional single window strategy, dual time window strategy takes it as the trigger condition in maintenance decision. It decides the best maintenance equipment combination based on the maintenance time window for unit grouping, and introduces a new maintenance time window for preventive maintenance implementation to optimize the system downtime. On the basis of dual time window, the optimal maintenance strategy of the system is obtained by minimizing total maintenance cost of the system during the planning period. The example analysis shows that the dual window strategy is better than the single window strategy on total maintenance cost.

In order to improve the ability of unmanned aerial vehicle (UAV) to detect obstacles, a fast edge detection algorithm is studied and an improved local binary pattern (I-LBP) operator is proposed to enhance the edge extraction effect. In HSV (Hue, Saturation, Value) color space, the I-LBP operator is used to describe the local texture features of pixels. The Hausdorff distance is used to confirm the edge pixels. And the contour of the obstacle is framed in the rectangular box. The algorithm improves the traditional LBP operator according to the fuzzy set theory. The simple binary description of the local texture features is extended to three-dimensional vector. It enhances the LBP operator’s ability to describe the local texture features and is noise-robust.The simulation verification is carried out with MATLAB. The results show that the I-LBP operator has good robustness and can identify obstacles under the condition of poor illumination and noise pollution. It also has high real-time ability, which can meet the requests of UAV in avoiding obstacles.

Aiming at the problem of various equipment and complicated correlations in operation system-of-systems under the information conditions, an evaluation method of equipment’s contribution rate to system-of-systems based on operation loop and self-information quantity is proposed from the perspective of system-of-systems effectiveness. The equipment and correlations are abstracted as nodes and edges. The edges’ metrics are determined according to the tactical and technical indexes of the nodes, and the network model of operation system-of-systems based on operation loop is constructed. According to the number and effectiveness of the operation loop involved in each target node, the method for evaluating the effectiveness of operation system-of-systems based on self-information quantity is proposed. And the corresponding evaluation model is built. Taking an anti-aircraft carrier operation system-of-systems as an example, the results show that the proposed method has fully taken the heterogeneity and uncertainty of each equipment and correlation into consideration to evaluate the equipment’s contribution rate to system-of-systems comprehensively and objectively. The proposed method can provide methodological support for equipment demonstration and operation system-of-systems optimization.

Collapse is one of the common disasters in the process of drilling and blasting tunnel construction. In order to effectively prevent the collapse accident and provide decision-making basis for safety risk analysis and management of tunnel construction, the T-S fuzzy fault tree and Bayesian network were combined, and an evaluation method based on both of them was proposed to calculate tunnel collapse possibility. According to the transformation of T-S fuzzy fault tree to Bayesian network, the Bayesian network model and conditional probability table were determined. In addition, fuzzy number and fuzzy subset were used to describe the fault state and probability of nodes respectively, and the bidirectional reasoning algorithm of Bayesian network was used to calculate. This method can use two different forward inferences to calculate the possibility of tunnel collapse, including the fuzzy subset of root node fault probability and the actual fault state in construction. At the same time, it can check the fault according to the result of importance analysis of root nodes, and can calculate the posterior probability of fault diagnosis of root nodes by back inference. Finally, the application of two engineering examples shows that the method can scientifically evaluate the possibility of tunnel collapse and determine key risk factors. This method can be used as a decision-making tool for tunnel construction safety assurance and management.

The preventive maintenance (PM) decision-optimization problem of complex equipment is studied under imperfect repair context aiming at the deficiencies of residual life prediction and cost estimation. Based on the virtual age theory, we built the relationship between maintenance efficiency and equipment age. Furthermore, three different methods are put forward to calculate the relation between checking intervals and maintenance period. The quality fluctuation and variable maintenance cost caused by fault are considered to build a more concise overall cost estimation model. A multi-objective decision-making model for preventive maintenance is established to ensure high availability of equipment and low total cost. Numerical verification is performed using the production and maintenance history data of a machining center. The verification intends to compare the different overall cost rate and equipment availability under static, dynamic and failure limits calculation methods and various maintenance levels. In the same way, the applicability of the three methods is discussed. The influence of the quality cost on the optimal strategy is analyzed to prove the advanced nature of the method.

The massive access of 5G base stations (5G BSs) provides new possibilities for the low-carbon development of future power systems. By incentivizing 5G BSs to participate in demand response and incorporating them into the existing active distribution network (ADN) operation framework, the cost of the electricity consumption of 5G BSs can be reduced while promoting the consumption and efficient use of renewable energy sources (RES). This paper proposes a multi-objective interval optimization model for ADN operation considering low-carbon empowerment of 5G BSs. Based on the interaction mode between 5G BSs and the distribution network, a 5G BSs operating flexibility description model is constructed, and the system dynamics method is used to reveal the mechanism of 5G BSs on carbon emission reduction on the distribution side. Taking the minimization of system operating cost and carbon emissions as the goals, and considering the constraints for both the distribution network and the communication network, a multi-objective optimization model for ADN operation with 5G BSs is established. The model cooptimizes the dispatch of RES and 5G equipment, and adopts an interval method to consider the uncertainty of RES output and communication loads, which can achieve simultaneous optimization of system economy and low-carbon benefits. Combining the equivalent transformation and the non-dominated sorting genetic algorithm to solve the problem, the results of numerical studies prove the effectiveness of the proposed method.

Wind-solar-electric vehicles coordinated optimization scheduling can effectively reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system. Most of the existing optimization scheduling models take the minimum equivalent load fluctuation as the optimization objective, which, only considering the overall fluctuation of equivalent load, cannot measure the matching degree of output-load, and do not consider the difference of output in different output scenarios. Therefore, a wind-solar-electric vehicles coordination scheduling method for high proportion new energy grid-connected scenarios is proposed. First, the disordered charging model of electric vehicles by Monte Carlo simulation is constructed. Then, a wind-solar output typical day classification model using Gap statistical and K-means++ is constructed based on the forecasting data of wind and solar power. Finally, taking the minimum equivalent load variance and load tracking coefficient as the double optimization objectives, a wind-solar-electric vehicles coordination optimization scheduling model is established, and the NSGA-II algorithm is used to solve it. The results demonstrate that the proposed model can effectively improve the matching degree of wind-solar output and load, and reduce the fluctuation of equivalent load, so as to reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system.

With the rapid development of distributed power generation research and application, the distributed trading market, as a new type of power trading mode, can effectively increase the consumption rate of renewable energy and is an important means to promote the realization of the goal of “carbon peaking and carbon neutrality”. Introducing the market evaluation mechanism into distributed transactions will prompt users to consider the impact of the market evaluation mechanism on their trading strategies and promote the sound development of the distributed transaction market. The distributed power trading market among micro-grid users is studied in this paper. First, taking the market participants and transaction supporting software and hardware as the research object, a multi-dimensional performance evaluation index system is established from the aspects of power supply capacity, user satisfaction, and platform security. Next, the research status of distributed power trading market evaluation methods is summarized. The key technologies of distributed power trading performance evaluation are analyzed from the establishment of index system, the index calculation method, and the comprehensive evaluation method. Finally, in combination with the current development status, the research direction of the distributed power trading performance evaluation in the future is prospected.

A day-ahead optimal decision-making model is established for an integrated electricity-heat energy system to participate in both the electric energy market and the spinning reserve market, and the step-by-step carbon trading is introduced into the proposed model. The conditional value at risk method is used to manage the uncertainty risk of renewable energy and electrical load. With the objective to minimize the operation scheme cost and carbon emission cost, an operation plan is developed and the reserve resources are arranged for the integrated electricity-heat energy system. The results of a case study show that the proposed model improves the reliability, economy, and low-carbon level by taking the complementary advantages of the integrated energy system and reasonably arranging reserve resources to deal with the risks caused by uncertain factors.

Serious earthquake disasters not only cause power outages in distribution network, but also destroy transportation networks, which hinders the transportation of resources for restoration of distribution network and slows down the restoration. This paper proposes an improved resilience evaluation method and a resilience enhancement strategy of distribution network considering the effects of seismic attack on transportation networks. First, a seismic attack model is established to describe the relation between earthquake disasters and failure probability of transportation-distribution networks based on peak ground acceleration. The impact of earthquake disasters on transportation-distribution networks is quantified, and the failure scenarios are generated. Then, a resilience evaluation index is proposed by introducing the waiting time for road repair of emergency repair teams. Afterwards, a bi-level optimization model for distribution network restoration considering the fault line repair, the road repair, and the emergency resource scheduling is established and solved. The upper layer aims at the minimum power loss load, while the lower layer takes the minimum waiting time of the repair team as the goal. Finally, case studies on a coupling example of a 12-node transportation network and an IEEE 33-node distribution network verify the feasibility of the improved resilience index and the effectiveness of the proposed method. The results show that the resilience index considering seismic attack on transportation networks is accurate, and the restoration strategy can effectively enhance the resilience of distribution network in earthquake disasters.

Driven by the carbon peaking and carbon neutrality goals, the power system is transforming to the new structure which is dominated by renewable energy and is facing a new supply-demand balance situation. Pumped storage, as the most mature energy storage technology at present, can provide flexible resources with different time scales to ensure the safety of the power system and promote the consumption of renewable energy. However, the operation strategy and cost sharing mechanism of the pumped storage station (PSS) are not clear, which hinders its further development under the new situation. In this context, the technical characteristics and functions of PSS are sorted out first. Then, the investment cost model is established from the perspective of the whole life cycle. After that, the evolution path of pricing mechanism and cost sharing mode are described in view of the different stages of electricity market development, providing a feasible scheme for the marketization of PSS. Finally, the future development of PSS is summarized and prospected.

Magnetorheological grease (MRG) has wide application prospect in the field of engineering due to the distinguished advantages such as, no settlement, simple sealing and simple preparation. The rheological properties of MRG are the basis for the design of magnetorheological absorber. In this study, lubricating grease based MRG with 30%, 50% and 70% weight fraction of carbonyl iron (CI) were prepared. Rheological properties including shear viscosity and shear stress of MRG with different CI weight fraction were examined from steady shear with and without magnetic fields applied. It is found that the constitutive relation of MRG can be described by Bingham model, and the higher the weight fraction of CI particles is, the wider adjustable range of shear viscosity and shear stress of MRG can reach. The critical strain between linear viscoelastic range and nonlinear viscoelastic range of MRG with 70% CI weight fraction obtained by using the large amplitude oscillatory shear method is 0.02%. In addition, the yield stress is 24.7kPa under the magnetic field of 0.96T; the maximum magneto-induced storage modulus is 1.17MPa; the relative magnetorheological effect reaches as high as 3814%.

Using robust eigenvalue method (REM), deviation propagation model was built to describe the relationship between the plane parameters and the concentricity of high pressure compressor (HPC) rotors in aero-engine assembly through experiments. The calculated values and measured values of parts eccentricity and circle run-out of mounting face were compared and the results verified the reliability of the model. In addition, contribution analysis was realized. In order to effectively improve the concentricity of HPC rotors, genetic algorithm (GA) was adopted to determine the optimal installation angles for each part. The results showed that the proposed REM-GA method was suitable for continuous process of stacked assembly and could improve the concentricity of each part by over 50 percent, and the obtained optimal installation angles were 6.225, 3.422 and 5.983 rad respectively.

The thermal mixed lubrication model of finite-length line contacts is established, which takes account of the elasto-plastic deformation of the asperities and boundary-film tribo-chemistry properties.The model is verified by comparing the infinite-length theoretical model, the elastohydrodynamic lubrication (EHL) test rig and double-disk experimental data.Then the effect of surface roughness and the lubricant properties on the lubrication and friction are preliminarily discussed. The results show that reducing the surfuce roughness can effectively improve the scuffing resistance under the current operating conditions.

The paper built a numerical model for analyzing friction of piston ring-liner system, which considered the influence of cavitation and mixed lubrication. To investigate the synergistic effect of low viscosity and surface texture, two types of low viscosity oils, i.e. SAE0W20 and SAE5W20, were selected. Besides, the friction of piston ring with spherical texture, which was with a radius of 30μm and a depth of 3μm, was compared to the friction of piston ring without surface texture. The results show that the impact of surface texture is closely related to the viscosity of lubricating oil. Under condition of lubricating oil SAE0W20, the spherical texture helps to reduce friction, while under condition of lubricating oil SAE 5W20, the spherical texture increases friction power loss and friction mean effective pressure.

Two-dimension (2-D) cosinusoidal phase-modulated scatter-wave jamming for multi-channel synthetic aperture radar-ground moving target indication (SAR-GMTI) is studied. First, based on the traditional scatter-wave jamming for SAR, 2-D cosinusoidal phase-modulated scatter-wave jamming theory is introduced. Then, the countering performance against SAR-GMTI is analyzed by using the dual-channel displaced phase center antenna (DPCA) technique. Finally, strong scattering baits are set as scattering targets to carry out computer simulation and application analysis. The research result shows that the jamming method can produce 2-D netted multi-false targets with real targets scattering information for multi-channel SAR-GMTI. In practical application, the distribution of jamming targets can be flexibly controlled by the rational allocation of jammers and bait targets, and the vivid moving false target jamming performance can be realized by the baits’ motion.

To deal with the fast position control problem of a howitzer shell transfer arm with load change and nonlinear friction disturbance, a nonsingular fast terminal sliding mode control strategy is designed combined with adaptive control. The dynamical equations of shell transfer arm with load change and nonlinear friction are established. To avoid singular problem of the control law and improve the convergence rate of reaching the sliding surface, a new nonsingular fast terminal sliding mode control strategy is used to design the control law of the howitzer shell transfer arm. An adaptive law is presented to estimate the unknown upper bound of the uncertain disturbance which is difficult to obtain. The state of the closed-loop system is finite time convergence based on the Lyapunov theory. In order to realize the friction compensation control, a genetic algorithm is used to identify the Stribeck model parameters of the system. The experiment results of shell transfer arm under three different load conditions show that the controller designed in this paper can position accurately, and it has a good robustness against load change and nonlinear friction. The correctness and effectiveness of the proposed control strategy are proved by experimental results.

The flight control strategy for vertical take-off and landing(VTOL) unmanned aerial vehicle(UAV) in mode transition is essential to ensure a safe and reliable flight. This paper studies the flight control strategy in the transition mode of tail-mounted VTOL UAV, and proposes a strategy of the fastest transition speed with constant altitude control. Simulation and experiment are used to analyze and compare the flight effect of this strategy, classical proportion-intergration-differentiation(PID) control and the fastest transition speed control. Strategy of the fastest transition speed with constant altitude control optimizes the change of flight speed and altitude, synchronizes the time of cruise attack angle and cruise speed, and maintains the vertical force balance of UAV in the mode transition. The simulation results show that the fastest transition speed with constant altitude control’s time decreases by 0.98 and 0.48s than that of the classical PID control and the fastest transition speed control, while the change of altitude decreases by 2.27 and 0.91m. The flight control effect of the fastest transition speed with constant altitude control is obviously superior to the classical PID control and the fastest transition speed control. The proposed control strategy solved the problem of large altitude change during the mode transition, and ensured the rapid and steady flight mode transition of UAV in the transition mode.

A method based on geometric distance of space vector is proposed for the self-collision detection in motion planning of dual-arm robots. In terms of this, a linear repulsion force field which is improved from the artificial potential field method is used to describe the position relationship and collision quota among the links. Then a novel self-collision-free motion planning is derived by combining with the linear form of repulsion potential field. Compared with the traditional artificial potential filed planner, the improved linear force field can directly act as an operator to plan the trajectory of the dual-arm robot with properties that are simple and easy to be handled. Finally, a case study demonstrates the effectiveness of the motion planning algorithm with the real parameters of a dual-arm robot.

In order to study the psychological characteristics of drivers under stress conditions, the electrocardiogram (ECG) data of drivers under different stress scenarios were analyzed. Using the Ergo LAB human environment synchronization platform, the driver’s heart rate variability (HRV) time domain data and frequency domain data were extracted, and the appropriate ECG characteristics were selected as indicators of analysis. By the statistical analysis method, the ECG data of the subjects were studied, and the psychological characteristics of the drivers under stress scenarios were obtained. It shows that in terms of the rate of increase in heart rate, different stress scenarios will have a significant impact on the driver’s heart rate growth. Drivers are sensitive to the target of pedestrians, bicycles, and less sensitive to other objects like the weather and motor vehicles. In terms of HRV time domain characteristics, the driver has a longer degree of tension about pedestrians and bicycles, and has intermittent tension about the weather or the vehicle. In terms of HRV frequency domain characteristics, the driver’s mental workload increases when he encounters pedestrians and bicycles, and reduces when he encounters motor vehicles and external weather conditions.

 The application of measured shortterm data to the prediction of longterm stability of weapon system is significant to shorten the production cycle of weapons. Considering such prediction problems as inadequate data and small sample sequence, optimized algorithm model was presented based on the drawback analysis of GM(1,1) prediction model. The optimized prediction methods were generalized as multiparameter prediction. At first, the model which used the latest measured data for initialization was established, followed by replacing the old information with the latest through metabolic approaches to realize equal dimension model predication. In addition, fading memory recursive least squares method was adopted for weighted handling of old and new information. The normalized mean relative error was used as accuracy test standard for background value and particle swarm optimization algorithm was adopted. Finally, the calibration parameters stability of a certain type of inertial measurement unit (IMU) was predicted, and the average relative error of the prediction results was reduced by 6%~58%. The results indicate that the prediction method can be applied to the longterm stability of IMU calibration parameters.

The manned aerial vehicle (MAV) in manned-unmanned aerial vehicle swarm (MUMS) can compensate for the inabilities of unmanned aerial vehicles (UAVs) in complex conditions. Taking the advantage of the information-efficient transmission of hierarchical interaction mechanism of pigeons, a novel method for consensus control of MUMS was proposed. According to the constraints and the human behavior characteristics, a dynamic model of UAVs and a multi-channel operator model were established, based on which, a hierarchical interaction network was constructed. In this network, the flight decisions of MAV affect the individuals with higher ranks. Then these UAVs affect the remaining UAVs through the leadership hierarchical network. Therefore, an adaptive containment control strategy was proposed to reduce the costs, and achieve motion consistency of MUMS. In addition, the stability of the system was analyzed. Moreover, the effectiveness of the proposed method was verified by simulations.

In order to evaluate the impact of shield tunnel construction on adjacent buildings, a method based on the fuzzy analytic hierarchy process (FAHP) and the interval number improvement technique for order preference by similarity to ideal solution (TOPSIS) is proposed. A risk assessment system based on soil properties, building factors, tunnel factors, shield tunneling parameters, and other factors is established after investigation. The FAHP is used to determine the weight of factors based on expert scoring. Based on the traditional TOPSIS method, 6 typical samples are selected according to the factor grading standard to determine the non-uniform risk rating criteria. For the first time, the interval improved TOPSIS method is applied to the risk assessment of shield tunneling beneath buildings. The engineering situation can be better represented by the interval number. Compared with traditional risk assessment methods, this method is more accurate, less affected by subjective factors, and more objective. The proposed method has been used to evaluate the risk of a certain masonry building, and the result is consistent with the actual situation, which proves its effectiveness of. Thus, the proposed method can provide reference for risk assessment of similar projects.

Aimed at the uncertainty of charging starting and ending point caused by incomplete charging and discharging in practical applications of lithium-ion battery, an estimation method of battery health based on dual charging state factors is proposed. A battery aging experiment bench is built, and eight nickel-cobalt-manganese lithium-ion batteries are subjected to aging test. Different from the traditional single state factor estimation, the average value of equal time difference current at the front end of constant voltage charging curve and the equal amplitude voltage charging time at the end of constant current charging curve are selected under different aging conditions to construct health factors. The corresponding relationship between state of charge (SOC) and open circuit voltage (OCV) of the experimental battery in different aging states is analyzed and the correctness of health factor is proved by theoretical deduction and experimental results. An improved support vector regression model with a strong generalization ability is established, and the hyperparameters of the model are optimized through the particle swarm optimization algorithm. The results show that the proposed dual-charging health factor is closely related to battery capacity aging and attenuation. The improved support vector regression model can estimate the health status in different aging states in real time, and has the ability to characterize local capacity rebound change, which can be used as an effective method for estimating the state of health of an embedded battery management system.

To respond to the demand of achieving carbon peaking and carbon neutrality goals, and to construct a complete “source-grid-load-storage” new energy power system, a distributed photovoltaic net load forecasting model based on Hamiltonian Monte Carlo inference for deep Gaussian processes (HMCDGP) is proposed. First, direct and indirect forecasting methods are used to examine the accuracy of the proposed model and to obtain spot forecasting results. Then, the proposed model is used to perform probability forecasting experiments and produce interval prediction results. Finally, the superiority of the proposed model is verified through the comparative experiments based on the net load data of 300 households recorded by Australia Grid. After obtaining the exact net load probabilistic forecasting results, the photovoltaic production can be fully utilized via power dispatch, which can reduce the use of fossil energy and further reduce the carbon emission.

The large-scale access to renewable energy such as wind power and photovoltaics brings great uncertainty in power system planning and operation. In order to enhance the ability of high-proportion renewable energy grid to respond to uncertain events and ensure the safe and economic operation, it is necessary to improve the flexibility of the power system. First, based on the perspective of line transmission capacity and safe operation, the flexibility index of the transmission line was defined. Next, considering the economic operation strategy of the system, a multi-objective transmission line planning model based on flexibility and economy was proposed to optimize the flexibility index, investment cost, operating cost, and renewable energy consumption. After that, the NSGAII optimization algorithm was used to solve the model. Finally, the improved Garver-6 and IEEE RTS-24 node systems were taken as examples to analyze the effectiveness of the proposed model. The results show that the planning scheme can improve the transmission capacity of power grids, reduce the probability of renewable energy abandonment, and improve the flexibility and economy of power grid operation.

To achieve the double carbon goal of “carbon peaking and carbon neutrality”, the construction of the power system which is based on the green energy needs to be accelerated. With the growth of the system scale, the distributed green energy carbon trading mechanism and the carbon data management technology based on the blockchain technology can effectively encourage the development of green energy and become effective means for the implementation of low-carbon electricity. The accurate and real-time carbon emission calculation will further provide data support for the accuracy and security of carbon trading information. First, the current research status of green certificate trading and carbon asset management is introduced. Next, the adaptability analysis of the key technologies of the blockchain technology in the four directions of green electricity traceability, green certificate trading, carbon trading, and joint market of green certificate and carbon assets is performed. Afterwords, the specific mathematical models of carbon emission calculation is studied, and the data availability of carbon source traceability methods applicable to the blockchain architecture are discussed. Finally, some suggestions for the future development of carbon emission flow analysis are proposed.

In structural strength topology optimization based on the variable density method, there are gray cells in the optimization result, making it difficult to accurately predict the structural stress which changes greatly before and after post-processing. This paper uses a filter-projection-based structural parameterization method to achieve a continuous decrease in the proportion of structural intermediate density units during the iterative optimization process. By studying the influence of the main optimization parameters of the structural ratio strength problem on the optimization process and structural strength optimization, a novel optimization strategy of structural topology optimization followed by approximate shape optimization is proposed, which realizes the accurate control of the change of structural stress during the optimization process, achieveing structural strength optimization while improving the stability of the optimization process. Typical optimization examples verify the rationality and practicability of the proposed optimization method.

In the dynamic and discrete ship block manufacturing process, lack of effective process resource organization and transparency in product processing leads to the problem of high cost and low efficiency for managers to acquire knowledge. A method for dynamic generation and updating of knowledge graph based on processing beat data flow is proposed. The definition of the processing beat data information model is defined by analyzing the processing flow and the station data characteristics of the ship blocks. The graph mapping steps, models, and fusion connection algorithms are proposed for static resources and processing beat data to realize the semantic association of station dynamic time series data and knowledge graphs. Based on the relationship between station process and product structure, the generation of workshop-level dynamic knowledge graph is realized. Taking the production process of a ship block as an example, the knowledge graph visualization prototype system is designed, developed, and verified. The results show that the proposed method is beneficial to the organization, acquisition, and reuse of knowledge in the process of ship block manufacturing.

Flow spinning is an advantageous technology in forming thin-walled cylindrical parts, but for cylindrical parts with inner ribs,the height of the inner ribs that can be formed is limited. Aimed at the problem that the inner ribs are difficult to fill in such components, a numerical simulation model of staggered spinning of thin-walled cylindrical parts with cross inner ribs is established based on Abaqus, and the influence of the number of rollers on the filling of inner ribs is analyzed. The process test of the cylinder part with orthogonal inner ribs is conducted. The results show that in the staggered spinning process, increasing the number of rollers will reduce the diameter expansion of the aluminum alloy cylindrical part with inner ribs, and inhibit the axial flow of materials, leading to a higher geometric accuracy of the inner ribs. The comparison of the simulation results and the test results shows that the average error of the inner rib filling fullness of simulation result is 11.3%, and the simulation model has a good reliability.

To improve the accuracy of photovoltaic (PV) power prediction, this paper proposes a novel weather classification method. First, it distinguishs the clear days and cloudy days according to the total cloud cover. Then, it further classifies the cloudy days into three subtypes to investigate whether the sun is obscured by clouds. This method can effectively identify the characteristics of key meteorological environmental factors that affect PV output and form a new classification index sky condition factor (SCF) by weighted summation. This method has clear physical meanings, good discrimination, and easy quantification. The reasonable classification of weather types can eliminate the coupling relationship between many meteorological environmental factors and reduce the dimension of input variables, which makes it easy for statistical modeling. Based on the theoretical and the statistical approachs respectively, the modeling and verification are conducted and the results show that the method can effectively improve the accuracy of PV power prediction.

Line hardening and energy storage configuration are important parts of the pre-disaster planning defense strategy, which can effectively improve the disaster prevention and emergency response capabilities of the hybrid AC-DC distribution system (HDS). Under the background of frequent extreme events, a method to improve the resilience of hybrid AC-DC distribution system considering line hardening and energy storage resource allocation is proposed, and a two-stage robust optimization model is constructed. Essentially, the model is a tri-level mixed integer nonlinear programming problem. The outer level evaluates the active behavior of HDS to determine the line hardening and energy storage system configuration strategies, the middle level determines the worst line failure set after the extreme event occurs, which is the passive behavior of HDS, and the inner level evaluates the active behavior of HDS to determine the emergency response and the operation strategies. Based on the nested column and constraint generation algorithm (nested column and constraint generation, NC&CG), the 3-level mixed integer linear programming model is solved. Finally, a simulation analysis is conducted with a 9-node DC distribution network and an improved IEEE-33 node hybrid AC-DC distribution system coupled with a ring AC distribution network as an example. The results show that the proposed method can effectively improve the resilience of the distribution network and ensure its safe and reliable operation in extreme events.