Top Read Articles

Published in last 1 year |

In last 2 years |

In last 3 years |

All

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

Inversion of Displacement Field of Marine Slender Pipelines Under Three-Dimensional Background Ocean Currents GUO Li, YUAN Yuchao, TANG Wenyong Journal of Shanghai Jiao Tong University    2025, 59 (12): 1815-1823.   DOI: 10.16183/j.cnki.jsjtu.2024.007 Abstract （4038）   HTML （18）    PDF（pc） （2176KB）（753）       Knowledge map    Save Marine pipelines are widely used in offshore engineering and are highly vulnerable to accidental damage caused by underwater structures such as ship anchors and deep-sea submersibles, especially in the dark and unpredictable marine environment. Research on configuration monitoring of marine pipelines is essential to ensure their operational safety. This paper develops a displacement field inversion model for marine pipelines under the influence of three-dimensional background ocean currents, based on the inverse finite element method. The model consists of an input parameter module, a coordinate conversion module, and a displacement reconstruction function module. It takes into account key characteristics such as large curvature, three-dimensional coupling with large displacements, and local flipping behavior. The proposed approach addresses the technical challenges associated with low-order deformation modes and irregular displacement patterns. The impact of the number and layout of monitoring points on the accuracy of displacement field inversion is studied. The results show that the layout with a monitoring point spacing of 100 m and an angle of 30° can meet the engineering accuracy requirements. The findings of this paper can provide valuable insights and methods for the design of marine pipeline health monitoring systems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Low-Carbon Energy Management in Active Distribution Networks Based on Dynamic Carbon Entropy WU Dongge, CHANG Xinyue, XUE Yixun, HUANG Yuxi, SU Jia, LI Zening, SUN Hongbin Journal of Shanghai Jiao Tong University    2025, 59 (12): 1795-1804.   DOI: 10.16183/j.cnki.jsjtu.2024.262 Abstract （3512）   HTML （8）    PDF（pc） （4000KB）（657）       Knowledge map    Save In the context of energy transition and “carbon peak and carbon neutrality” goal, active distribution networks in the new power system can achieve scalable energy conservation and emissions reduction by increasing the penetration of renewable energy and leveraging demand-side management. To this end, a two-stage low-carbon energy management strategy for active distribution networks based on dynamic carbon entropy theory is proposed, using carbon price as a price signal to guide flexible loads in participating in low-carbon demand response. First, the carbon entropy model is analyzed, and a carbon entropy model considering energy storage is established to refine the carbon emission characteristics on the demand side. Then, an evaluation index of node carbon potential is proposed to evaluate the cleanliness of carbon emission of the system. Next, a two-stage low-carbon optimization scheduling model is developed for active distribution networks based on the dynamic carbon entropy. By utilizing the time-of-use electricity prices and node carbon prices as guiding signals, the model promotes the renewable energy consumption, reduces system carbon emissions, and achieves a certain peak-shaving and valley-filling effect. Finally, multiple scenarios are set up in the IEEE 33-node system to validate the effectiveness and superiority of the proposed low-carbon energy management model. The results show that the proposed strategy can achieve low-carbon energy management in active distribution networks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Stability Analysis of LCL Grid-Connected Inverter Based on Neural Network HAN Hualing, JIA Yichao, MA Zihan, DENG Jun, HUANG Meng Journal of Shanghai Jiao Tong University    2025, 59 (12): 1805-1814.   DOI: 10.16183/j.cnki.jsjtu.2023.653 Abstract （3219）   HTML （14）    PDF（pc） （2261KB）（1028）       Knowledge map    Save The parameter uncertainty of LCL type grid-connected inverter can significantly affect the power quality of renewable energy, making it essential to analyze the stability of the inverter under the parameter inception. To solve these problems, this paper establishes a state space model of LCL type single-phase grid-connected inverter and proposes a neural network modeling method based on the parameter model. Through the parameter characterization, a training dataset based on the parameter distribution is obtained. This dataset is then trained in the neural network to produce the stability discrimination results. Finally, the effectiveness of the proposed method is verified by MATLAB/Simulink simulation and the experimental platform. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Location of Partial Discharge in GIS Based on Electromagnetic Wave Time Reversal LI Jiayang, ZHAO Jiuyi, QIAN Yong, LI Guoyu, XU Zhiren, PAN Chao, SHENG Gehao Journal of Shanghai Jiao Tong University    2025, 59 (12): 1763-1772.   DOI: 10.16183/j.cnki.jsjtu.2024.038 Abstract （3059）   HTML （21）    PDF（pc） （6887KB）（828）       Knowledge map    Save In recent years, as an effective positioning method, electromagnetic wave time reversal (EMTR) technology has begun to be applied in the field of partial discharge. Compared with traditional ultra high frequency (UHF) positioning methods, EMTR technology requires only a single sensor, offering significant advantages and promising application prospects. However, the commonly used maximum field strength and minimum entropy criteria struggle to accurately determine the focusing time and position of time reversal in complex structures. To address these problems, this paper proposes an EMTR location method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm leveraging the distinct characteristics of waveforms at signal and non-signal sources, and verify the location of partial discharges in gas-insulated switchgear (GIS) using CST Studio Suite simulation software. To verify the field feasibility of EMTR, it develops a laboratory model to conduct partial discharge experiments. The results show that the average positioning error of the EMTR method is less than 20 cm, which can realize accurate positioning of partial discharge sources in GIS. Compared with traditional methods, the EMTR method reduces the number of sensors and improves the anti-interference performance, which has certain advantages. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

A Review of Optimal Allocation and Operation of Energy Storage System for Peak Shaving and Frequency Regulation in New Type Power Systems FENG Mengyuan, WEN Shuli, SHI Shanshan, WANG Haojing, ZHU Miao, YANG Wen Journal of Shanghai Jiao Tong University    2026, 60 (1): 1-18.   DOI: 10.16183/j.cnki.jsjtu.2024.128 Abstract （2757）   HTML （35）    PDF（pc） （3194KB）（1690）       Knowledge map    Save To achieve China’s “dual carbon” goal, integrating large-scale renewable energy into power grids has become an irreversible trend. With the continuous increase in the use of renewable energy, the wind and solar power integration poses critical challenges to the stable operation of the power system. With the perfect dynamic response of active and reactive power, energy storage system can smooth power fluctuations caused by intermittent and uncertain renewable energy, which is conducive to promoting the access of large-scale new energy, realizing the smooth load regulation, and improving the interactive friendliness of the power grid. First, starting from the development of energy storage technology, this paper introduces the domestic and foreign research status of energy storage participating in the auxiliary service market of power peak regulation and frequency modulation. Then, it conducts a comprehensive review on the optimization configuration of energy storage systems taking into account peak shaving and frequency regulation requirements, analyzing from two perspectives: single-type setup and hybrid energy storage. Additionally, it summarizes the solving algorithms for the optimal configuration of energy storage systems. Afterwards, it proposes a grid-friendly new power system based on energy storage participation, and elaborates on collaborative scheduling methods and control strategies in multiple time scales and multiple regions from the perspective of collaborative operation. Finally, it provides an outlook on the future research direction of energy storage from four aspects, which are shared cloud energy storage, numerical intelligent aggregation modeling,intelligent and adaptive control technology, and improving multi-regional cooperation and standardization policy mechanism. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Modeling Methods for Power Secondary System Simulation in New Power Systems HE Ruiwen, XIE Haijun, LU Jialiang, YANG Changxin, MOHAMMAD Shahidehpour Journal of Shanghai Jiao Tong University    2025, 59 (11): 1581-1591.   DOI: 10.16183/j.cnki.jsjtu.2023.556 Abstract （2719）   HTML （34）    PDF（pc） （2421KB）（1303）       Knowledge map    Save New power system achitecture will greatly increase the difficulty and vulnerability of the operation and control of power systems. The high integration of information and communication technology (ICT) promotes comprehensive information sharing, but it also highlights the urgency of establishing modeling and analysis methods for ICT-based power secondary systems. In this paper, simulation modeling methods for power secondary systems are proposed for the first time to achieve information sharing under interconnectivity and interoperability criteria. A smart substation secondary system with complex functional descriptions is taken as the research object. First, a structural model of intelligent electronic devices (IEDs) is proposed which meets the interconnectivity requirements. Then, a functional model of IEDs with built-in algorithms for secondary business in power systems is proposed, as well as power communication protocol models which meet the interoperability requirements under IEC 61850 standard. Furthermore, the IED function in the node domain and data exchange between IEDs in the network domain are achieved, through the state design in the process domain. Finally, taking a typical 220 kV substation line current protection as an example, the entire process of protection setting modification and protection actions after a fault occurs are simulated by correlating the operating status of the power primary system, verifying the correctness of the proposed simulation models. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Design and Optimization of Freight Railway Energy Storage Traction System for Time-Sharing Cross-Regional Peak Shaving and Valley Filling YANG Huanhong, YANG Zhenyu, HUANG Wentao, CHAI Lei, WANG Yuxuan, YE Jingyuan Journal of Shanghai Jiao Tong University    2025, 59 (12): 1784-1794.   DOI: 10.16183/j.cnki.jsjtu.2024.016 Abstract （2616）   HTML （13）    PDF（pc） （3468KB）（762）       Knowledge map    Save To address the difficulties in adjusting new energy resources along electrified railways, uneven load distribution, and waste of potential energy in loading and unloading operations after trains arrive at stations, a design optimization strategy is proposed for the energy storage traction system of cross-regional freight railways. A time-sharing zoning electricity price model and an energy storage traction system capacity optimization allocation model are developed to guide load time shifting to achieve peak shaving and valley filling in different time domains and geographical spans by analyzing the spatiotemporal characteristics of mobile energy storage charging and discharging. Then, an energy management strategy is proposed aiming at maximizing the daily operating efficiency. The calculation example verifies that the designed energy storage traction system and its operation strategy can effectively improve the economic benefits during the operation cycle of the train, promote energy consumption along the line, and have important reference value for the low-carbon operation of electrified railways. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Maneuvering Motion Modeling of Unconventional Ship Based on Numerical Calculation ZHENG Mao, DING Shigan, LAN Jiafen Journal of Shanghai Jiao Tong University    2025, 59 (12): 1824-1836.   DOI: 10.16183/j.cnki.jsjtu.2024.053 Abstract （2578）   HTML （12）    PDF（pc） （14519KB）（1011）       Knowledge map    Save To develop a maneuvering model for a typical unconventional ship, a decoupled modeling approach was adopted as the foundation to construct a numerical calculation based maneuvering model framework. To determine the hydrodynamic derivatives for the maneuvering model, static oblique towing test (OTT) and dynamic circular motion test (CMT) were performed by using numerical simulations to obtain the forces on ship body. To improve the accuracy of nonlinear hydrodynamic derivatives, a hybrid method combining cubic spline interpolation with least squares fitting was proposed. The zigzag and rotation tests of the scaled ship model verified the accuracy of the maneuvering model. The results show that the maneuvering motion model established by numerical calculation and the hybrid method has good accuracy and can be used for modeling unconventional ship maneuvering motion. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

An Evolutionary Game Approach to Incentive Mechanism of Vehicle-to-Grid PAN Yi, WANG Mingshen, MIAO Huiyu, YUAN Xiaodong, HAN Huachun Journal of Shanghai Jiao Tong University    2025, 59 (11): 1637-1646.   DOI: 10.16183/j.cnki.jsjtu.2023.603 Abstract （2377）   HTML （24）    PDF（pc） （4079KB）（3070）       Knowledge map    Save Electric vehicles (EVs) can provide significant support for the flexible operation of power systems, in which vehicle-to-grid (V2G) mode is an important way for EVs to participate in the frequency and voltage regulation of power grids. However, the commercialization of V2G has experienced slow progress to date, and the lack of an effective market operation mechanism makes it difficult for large-scale EVs to participate in the ancillary services of the grid. Therefore, a novel evolutionary game model is proposed with the participation of the electricity regulatory commission, power grid company, and EVs and the impact of the strategic choices of the three parties on the operation of the V2G market is explored to identify the subsidy and pricing mechanisms for the government to facilitate the long-term evolution of the V2G. First, replicator dynamic equations for the game are established to investigate the stability of multiple strategy equilibrium points in the three-party evolutionary game. Then, the Lyapunov stability theory is employed to analyze the stability of these equilibrium points and to determine the subsidy amount to promote V2G development. Next, a simulation analysis is conducted on the actual electricity price data from Shanghai in China, which quantitatively identified the government subsidy coefficient range and electricity price range to incentivize EV participation in the V2G model. The simulation results provide theoretical support for the electricity regulatory commission and power grid company in formulating subsidy and pricing strategies. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Kinetic Analysis of High-Power Fuel Cells Based on Electrochemical Impedance Spectroscopy ZHU Dong, PENG Linfa, QIU Diankai, YANG Miao Journal of Shanghai Jiao Tong University    2026, 60 (8): 1227-1237.   DOI: 10.16183/j.cnki.jsjtu.2025.027 Abstract （2374）   HTML （15）    PDF（pc） （10156KB）（1021）       Knowledge map    Save Owing to the merits of non-destructiveness, wide frequency range, high precision, and applicability in vehicle-mounted systems, electrochemical impedance spectroscopy (EIS) has been widely applied in research on proton exchange membrane fuel cells (PEMFCs) in recent years. However, the impedance measurement and quantitative analysis for high-power fuel cell stacks remain understudied. In this paper, EIS measurement is performed on high-power fuel cell stacks under standard operating conditions. The impedance spectra are analyzed using fast Fourier transform and total harmonic distortion. The results indicate that the direct current-direct current (DC-DC) converter generates harmonic interference at high frequencies. Furthermore, a distribution of relaxation time method based on characteristic frequency resolution optimization is adopted to tune the relevant resolution and regularization parameters, enabling the quantitative identification of the kinetic polarization processes in high-power fuel cell stacks, including proton transport in the catalyst layer, anode polarization, charge transfer, and oxygen mass transfer processes. The proposed method for EIS measurement and quantitative analysis provides technical support for the quantitative identification of the kinetic polarization processes in high-power fuel cell stacks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Neural Branching Power-Computation Network Fast Optimization Method Considering Computing Demand Response ZHANG Lei, LI Ran, TANG Lun, CHEN Sijie, ZHAO Shizhen, SU Fu Journal of Shanghai Jiao Tong University    2025, 59 (11): 1592-1602.   DOI: 10.16183/j.cnki.jsjtu.2023.616 Abstract （2361）   HTML （22）    PDF（pc） （2004KB）（1002）       Knowledge map    Save The rapid development of data centers makes it possible for them to participate in power system dispatching as demand response. By dispatching the computing resources in data centers between regions, it is possible to achieve energy saving, emission reduction, and cost saving. However, considering the demand response of data center computing resources in power system dispatching faces the problem of insufficient computing speed. To address this issue, a neural branching power-computation network fast optimization method considering computing demand response is proposed. First, a unit commitment double-layer model considering computing power resource demand response is established. Then, the graph convolutional neural network and the branch and bound method are combined and applied to the double-layer model. Through historical data training, the neural branching power-computation network fast optimization method considering computing demand response has the ability to quickly determine the order of branch and bound variables and minimize the number of iterations, which significantly improves the solution speed and realizes the fast solution of unit combination demand response considering data center computing resources. The performance of the proposed method is verified in the simulation scenario of “east data, west computing” project. The proposed method achieves an average solution time reduction of 39.1% compared to the pseudo-cost branching algorithm, 38.1% compared to the commercial solver CPLEX, and 13.5% compared to the machine learning-based optimization acceleration algorithm Extratrees, the average solution time is reduced by 13.5%. In addition, the system coordinated dispatching frequency is increased from 1 time/h to 4 times/h, and the maximum potential reduction in wind curtailment of the total wind power generation during 24 h accounts for 17.42%. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Information Gap Decision Theory-Spectrum Clustering Typical Scenario Generation Considering Source Load Uncertainty ZHANG Jiamin, CAI Ye, TANG Xiafei, TAN Yudong, CAO Yijia, LI Junxiong Journal of Shanghai Jiao Tong University    2025, 59 (11): 1625-1636.   DOI: 10.16183/j.cnki.jsjtu.2023.580 Abstract （2353）   HTML （13）    PDF（pc） （2201KB）（1098）       Knowledge map    Save The high proportion of new energy and dynamic load brings significant bidirectional uncertainty of source and load to power system. The strong uncertainty makes scheduling planning face high dimensional decision space and increases planning risk. Therefore, an information gap decision theory (IGDT)-spectral clustering typical scenario generation method considering the uncertainty of source load is proposed to provide a more accurate planning scenario for the determination of the operation mode of multi-source joint systems. First, the source load uncertainty can be quantified effectively by the IGDT theory without considering the uncertain quantitative probability distribution. The source load fluctuation range is described using the IGDT robust model and the IGDT chance model, and the original scenario representing each uncertainty situation is generated using the Latin hypercube sampling method to ensure the adequacy and accuracy of sample space. Then, to address the huge scale of the original scenario caused by the uncertainty of the source load, a spectral clustering method considering the adjustment ability of the system is introduced to mine the feature vectors of different source load fluctuations which have an important impact on the scheduling decision to reduce the complex original scenario set to a representative typical scenario of the source load. Finally, the simulation analysis of the actual system and operation data of a provincial power grid shows that compared with the traditional spectral clustering method, the proposed method generates four more typical scenarios after considering the bidirectional uncertainty of source load, the comprehensive average Pearson correlation coefficient is increased by 8.76%, the comprehensive average Euclidian distance is reduced by 43.48%, and the clustering scenario is more similar to the actual scenario. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Bi-Level Optimization Scheduling Strategy for Building Integrated Energy System Considering Virtual Energy Storage LIU Donglin, ZHOU Xia, DAI Jianfeng, XIE Xiangpeng, TANG Yi, LI Juanshi Journal of Shanghai Jiao Tong University    2026, 60 (1): 61-73.   DOI: 10.16183/j.cnki.jsjtu.2024.036 Abstract （2287）   HTML （14）    PDF（pc） （2529KB）（769）       Knowledge map    Save Integrated energy systems in buildings are an effective means to achieve low-carbon buildings. To further tap into their demand-side flexibility adjustable potential and carbon reduction potential, and reasonably allocate the interests of various entities in the building integrated energy system, a bi-level optimization scheduling strategy for building integrated energy system considering virtual energy storage in buildings under Stackelberg game framework is proposed. First, the thermal inertia of the cooling and heating system inside the building and the flexibility of the cooling and heating load are considered to leverage the virtual energy storage function of the building and improve system flexibility in the game model. Then, the genetic algorithm is used to solve the upper-level pricing model of energy operators, updating the purchase and sale electricity prices set by upper-level leaders, while the CPLEX solver is used to solve the lower-level problem, optimizing equipment output, demand response, and electricity trading plans. Finally, the proposed model is verified by case studies that it can effectively improve the economic performance and low-carbon characteristics of building integrated energy systems. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Inter-Provincial Tie-Line Power Assessment Mechanism and Model for Spot Market CHEN Zijie, SONG Bingbing, LI Yutong, WANG Lifeng, TENG Xiaobi, YAN Zheng, CHEN Sijie Journal of Shanghai Jiao Tong University    2025, 59 (12): 1773-1783.   DOI: 10.16183/j.cnki.jsjtu.2023.643 Abstract （2255）   HTML （16）    PDF（pc） （1795KB）（674）       Knowledge map    Save Multiple regional power grids in China have adopted the control performance standard (CPS) to assess the performance of inter-provincial tie-line operations, which has played a significant role in the reduction of both power deviation on inter-provincial tie lines and frequency deviation across the grid. However, in recent years, during peak electricity consumption months, the provinces in recipient regions often face a collective shortage of frequency regulation resources, while inter-provincial electricity spot market prices remain high. The current CPS assessment mechanism, which calculates CPS penalties based on a lower fixed price, has become ineffective as a safe guard for short-term power and frequency balance. Therefore, a master-slave game model between the regional dispatch center and provincial dispatch centers is proposed to determine the CPS assessment price that ensures sufficient reservation of automatic generation control (AGC) adjustable capacity. In this model, the regional dispatch center sets the CPS assessment price, and the provincial dispatch center responds by formulating day-ahead dispatch plan based on this price. The regional dispatch center adjusts the assessment price based on the dispatch optimization results of the provincial center until the adjustable capacity or frequency deviation of the province meets certain requirements. Case study results show that when the assessment price is set at eight times the highest value of various market prices, the assessment mechanism effectively incentivizes provincial dispatch centers to purchase inter-provincial spot electricity and demand response resources in face of generation shortage. This approach enables the system to meet the maximum predicted load and potential forecast errors, thereby maintaining the frequency deviation of the interconnected power grid within acceptable limits. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Application of Digital Twin-Based Improved A*HNSA Algorithm to Ship Block Hoisting Tasks QI Linlong, LIU Liquan, WANG Zhe, ZHANG Zishen, ZHU Ying, XIA Tangbin Journal of Shanghai Jiao Tong University    2026, 60 (8): 1266-1278.   DOI: 10.16183/j.cnki.jsjtu.2025.353 Abstract （2253）   HTML （1）    PDF（pc） （14852KB）（392）       Knowledge map    Save Block yard scheduling is a crucial link in ship construction. To address the issue of low efficiency in yard hoisting operations, this paper constructs a digital twin system and a high-fidelity virtual simulation environment for block yards, and proposes an improved two-stage optimization method named A*HNSA. Driven by the digital twin, this method jointly optimizes the hoisting sequence and the gantry crane path planning. Based on the real scheduling data from a shipyard in Shanghai, three types of test cases (small, medium, and large) are established according to the number of scheduling blocks, and comparisons are made with existing methods. The results show that, compared with existing methods in the three types of test cases, the proposed method reduces the total transportation distance by 3.51%, 5.33%, and 6.79% respectively, and shortens the total scheduling time by 3.82%, 5.35%, and 7.07% respectively. It significantly reduces the transportation cost of gantry cranes, improves the solution efficiency, and meets the online response requirements of the block yard digital twin system. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Active Sonar Target Depth Estimation Method Using Echo Structure in Bottom Bounce Area XIE Liang, WANG Lujun, WANG Zhuoran Journal of Shanghai Jiao Tong University    2025, 59 (12): 1847-1854.   DOI: 10.16183/j.cnki.jsjtu.2023.605 Abstract （2245）   HTML （11）    PDF（pc） （5332KB）（672）       Knowledge map    Save The discrimination and recognition of underwater targets has been the focus of active sonar detection. The structural characteristics of the target echo arrival in the bottom bounce area are analyzed when the target depth is greater than the transmit-receive depth. A 9-path arrival structure model is established. The relationship between the time delay of the bottom reflected/ surface-bottom reflected path and the target depth is analyzed. A method is proposed to estimate the time delay by searching for target arrival structure in bottom bounce area, and to estimate the target depth by using the relationship between the time delay and the target depth. The algorithm is verified based on explosion sound source data. The estimated depth of the underwater target obtained by a single hydrophone is in good agreement with the real target depth. The depth estimation of 20 targets within the range of 2.0—25.6 km is conducted, among which the depth estimation error of 17 batches is less than 10 m, and the average depth estimation error of 20 batches is approximately 7 m. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Influence Mechanism of Droplet Re-Entrainment in Wire Mesh Filter for Marine Gas Turbine FEI Yunda, LIU Yanming, WANG Jianhua, SUN Shijun Journal of Shanghai Jiao Tong University    2025, 59 (12): 1837-1846.   DOI: 10.16183/j.cnki.jsjtu.2024.033 Abstract （2214）   HTML （12）    PDF（pc） （24638KB）（621）       Knowledge map    Save The re-entrainment of droplets in the inlet filtration components of marine gas turbines severely affects the gas intake quality and the safe operation of gas turbines. To address this issue, the formation and influence mechanisms of droplet re-entrainment in the wire mesh filter were studied, and a comparative analysis was conducted on the effects of different inlet air velocities and droplet diameters on the liquid film thickness on the surface of the mesh and the mass of re-entrainment. The results show that incoming droplets tend to deposit upstream of the mesh segment and form a liquid film due to velocity gradients and inertia effects. Liquid film stripping is the main form of re-entrainment under marine operating conditions, occurring at a critical inlet air velocity between 4 and 4.5 m/s. The compact arrangement of adjacent wire mesh layers accelerates the airflow, and intensifies shear effect on the liquid film, which in turn increases the stripping film mass. This should be avoided during mesh fabrication. With the increase of the inlet air velocity, the overall thickness of the liquid film decreases, while the mass of stripping film increases. The increase in droplet diameter leads to easier blockage of the mesh pores and the local increase in film thickness, eventually leading to the overall decrease in film thickness but the increase in stripping film mass, which seriously affects the filtration efficiency. At a droplet diameter of 20 μm, the filter fails. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Collaborative Optimization Scheduling Method for Active Distribution Networks Considering Dispatchable Backup Batteries of 5G Base Station and Soft Open Point GAO Chong, DUAN Yao, CHENG Ran, CHEN Peidong, ZHOU Shucan, ZHANG Shenxi, CHEN Weilin, LIU Zhiwen Journal of Shanghai Jiao Tong University    2025, 59 (11): 1603-1617.   DOI: 10.16183/j.cnki.jsjtu.2024.020 Abstract （2139）   HTML （14）    PDF（pc） （4061KB）（872）       Knowledge map    Save As the proportion of distributed generation, mainly wind turbine generation and photovoltaic, in terminal energy consumption increases, it is of great significance to fully utilize the flexibility resources within power grids and enhance the regulation capability of active distribution networks (ADN). To this end, an ADN collaborative optimization scheduling method is proposed considering dispatchable backup battery of 5G base station (BS) and soft open point. First, an analysis of the power consumption model of 5G BS is conducted, leading to the establishment of a backup battery capacity evaluation model which considers the communication load of 5G BS and the reliability of ADN nodes. Based on this and taking the minimization of the comprehensive operating cost of ADN as the objective function, considering the uncertainty of wind power, photovoltaic output, and load demand, an ADN collaborative optimal scheduling model based on chance constraints is developed. A second-order cone relaxation and a chance-constrained determinization approach based on Latin hypercube sampling are employed to enhance the solution efficiency of the model, which transforms the model into a mixed-integer second-order cone programming problem. Finally, the feasibility and effectiveness of the proposed method are verified by the IEEE 33-bus ADN case. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Two-Stage Optimization Strategy Considering Ship Resilient Voyage Programming and Power Generation Scheduling ZHOU Lidan, ZHENG Hang, YU Tianyou, WANG Jie Journal of Shanghai Jiao Tong University    2026, 60 (6): 892-903.   DOI: 10.16183/j.cnki.jsjtu.2024.275 Abstract （2114）   HTML （5）    PDF（pc） （2545KB）（538）       Knowledge map    Save For oceanic island clusters with special geographical conditions, this paper proposes a rescue vessel scheduling strategy that integrates island transportation and energy supply to address resource heterogeneity among oceanic island clusters and ensure vessel safety, thereby improving the disaster resilience of rescue vessels under optimized scheduling. Based on both energy dispatch and transportation, a two-stage optimization method is adopted. In the first stage, day-ahead scheduling is performed according to the geographical relationship of the islands to determine preliminary sailing routes. In the second stage, both safe and emergency modes are considered. In the safe mode, based on the optimized routing information from the first stage, further optimal scheduling of power generation is performed to improve operational efficiency and economic performance under environmental constraints. In the emergency mode, considering generator fault, a coordinated optimization strategy between load-side management and power generation is implemented to support critical rigid loads and guarantee the normal operation of rescue vessels. Finally, simulation results verify the effectiveness of the proposed strategy, providing a theoretical reference for the sustainable development and construction of oceanic island clusters. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Frequency Regulation Trading Bidding Dispatch for Internet Data Center with Energy Storage Considering Physical-Virtual Energy Storage Coupling ZHAO Jiayi, HUANG Chunyi, WANG Chengmin, LI Kangping, LI Zhao, TIAN Zhuangmei Journal of Shanghai Jiao Tong University    2026, 60 (6): 871-881.   DOI: 10.16183/j.cnki.jsjtu.2024.358 Abstract （2100）   HTML （16）    PDF（pc） （2717KB）（635）       Knowledge map    Save To address the issues of insufficient modeling accuracy of the adjustable capacity of Internet data centers with energy storage (IDCE) and the lack of coordination between energy storage systems and temperature-controlled server room loads in existing studies, which limit the full utilization of IDCE regulation potential under both normal and extreme grid operating conditions, this paper proposes a frequency regulation trading bidding dispatch method for IDCE considering physical-virtual energy storage coupling, aiming to explore a normalized profitability strategy for IDCE while ensuring basic operational requirements. First, based on workload dispatch characteristics and the virtual energy storage behavior of server rooms, the coupled operation mechanism between physical and virtual energy storage within IDCE is analyzed, and a corresponding coupled adjustable capacity model is developed. Then, to maximize daily operating revenue, a frequency regulation trading and bidding dispatch model is established considering workload uncertainty and frequency regulation service compensation prices. The nonlinear constraints from the server power consumption model based on dynamic voltage-frequency regulation are addressed using a relaxation approximation approach. Finally, the effectiveness of the method proposed in this paper is validated through a numerical case study. The proposed method can realize regular profitability of IDCE on the premise of meeting basic operation requirements. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bi-Level Levelized Cost Model for Grid-Connected Wind-Photovoltaic-Storage Hydrogen Production System ZHANG Dong, JIANG Dongfang, LIU Chenxi, YOU Peiyu Journal of Shanghai Jiao Tong University    2026, 60 (6): 955-964.   DOI: 10.16183/j.cnki.jsjtu.2025.150 Abstract （1983）   HTML （3）    PDF（pc） （2720KB）（469）       Knowledge map    Save High electricity costs remain one of the primary obstacles limiting the large-scale deployment of wind-photovoltaic (PV) hydrogen production systems. In such systems, the output characteristics of renewable power, the wind-PV-storage-load configuration ratio, and the grid-connected/off-grid operation mode affect the utilization rate of renewable power generation and the electricity consumption structure for hydrogen production, thereby influencing the variable and fixed costs per unit of hydrogen production. This paper develops a bi-level levelized cost of hydrogen (BLCOH) analysis model for grid-connected wind-PV-storage hydrogen production systems, explicitly considering dynamic variations in renewable energy supply and hydrogen production electricity demand. The model is applied to compare the costs of three system configurations: off-grid wind-PV hydrogen production, off-grid wind-PV-storage hydrogen production, and grid-connected wind-PV-storage hydrogen production. Furthermore, the impacts of renewable energy utilization hours, hydrogen production utilization hours, and peak-shaving electricity pricing on hydrogen production costs are quantitatively analyzed. The results indicate that integrating energy storage improves renewable energy utilization and enables additional revenue through grid peak-shaving services, while grid connection enhances the operational reliability of hydrogen production systems. Compared with the other two approaches, the grid-connected wind-PV-storage hydrogen production system achieves lower hydrogen production cost, making it promising in the future. Additionally, this paper derives analytical expressions for estimating critical thresholds of storage cost, grid electricity price, and the combined cost of storage and grid connection for wind-PV hydrogen production systems. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Object Detection of Steel Mesh Binding Point Using FNB-YOLOv5 LI Zixuan, ZHAO Zhigang, ZHANG Zeyu, JIE Junjie, CHENG Ruiqiang Journal of Shanghai Jiao Tong University    2026, 60 (5): 762-775.   DOI: 10.16183/j.cnki.jsjtu.2024.121 Abstract （1972）   HTML （13）    PDF（pc） （24548KB）（1027）       Knowledge map    Save To address the problems of low accuracy and slow detection speed in existing target detection algorithms used by rebar-binding robots for identifying binding points, an improved rebar mesh binding-point detection method named FNB-YOLOv5, was proposed based on enhancements to YOLOv5. First, a dataset of rebar grid intersections was created through image acquisition. Then, the lightweight FasterNet backbone was incorporated into the YOLOv5 network to enhance feature extraction while reducing network complexity. Next, the BiFormer attention mechanism was introduced into key network components to improve the accuracy of feature extraction. Considering that small-sized targets dominate the detection task, the NWD loss function was used for normalization to optimize the localization accuracy of binding-point detection. Finally, an improved F-global feature pyramid network (F-GFPN) feature fusion module was devised to enhance feature interaction and improve computational performance by incorporating skip connections and cross-scale connections. Comparative experiments with different models show that the proposed method achieves a precision of 99.82%, a recall of 99.10%, and a mean average precision of 98.64%, which represent an increase of 2.9 percentage points, 1.53 percentage points, and 1.63 percentage points over the original model, respectively. The frame per second (FPS) reaches 44.8, an increase of 4.1 compared with the original model, while the size of weight model is reduced by 2.93 MB. Experimental results demonstrate that the improved FNB-YOLOv5 model achieves higher accuracy and real-time performance on the rebar binding point dataset, providing technical support for the development of rebar binding robots in the construction industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Regulation Method for Active Distribution Network of Electric Vehicles Considering User Travel Uncertainty CAI Muliang, FAN Ruixiang, HE Guidong, ZHU Yan, CHE Liang Journal of Shanghai Jiao Tong University    2026, 60 (6): 882-891.   DOI: 10.16183/j.cnki.jsjtu.2024.312 Abstract （1956）   HTML （13）    PDF（pc） （2590KB）（575）       Knowledge map    Save To address the issues of the insufficient regulation resources and flexibility in current active distribution networks, this paper proposes a two-stage regulation method for electric vehicle (EV) participation in the optimization of active distribution networks. Considering the impact of EV regulation on charging demand of users under the uncertainty of their travel behavior, the proposed method intends to fully exploit the regulation potential of EVs while reducing the impact of regulation on the travel of vehicle owners. First, a power deviation index is established to characterize the impact on EV charging behavior, and EV aggregation is performed with the power deviation taken into account. Then, the optimal regulation of active distribution networks containing EV aggregations, energy storage systems, and distributed generation units is conducted. Finally, the aggregated EV charging/ discharging commands are disaggregated to individual EVs by minimizing user-side impact. The effectiveness of the proposed method is validated through simulation on the IEEE 33-bus system. Numerical experiments show that the strategy can balance the regulation capability of EVs and charging demand of users. Compared with the traditional method, the EV power deviation is reduced by 25%, and the accommodation capacity is increased by 57%. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Co-Control Method for Direct Current Microgrids with Electro-Hydrogen Coupled Energy Storage Systems LI Jianlin, SHI Zelin, LIANG Zhonghao, LIANG Ce Journal of Shanghai Jiao Tong University    2026, 60 (6): 943-954.   DOI: 10.16183/j.cnki.jsjtu.2024.293 Abstract （1914）   HTML （2）    PDF（pc） （3822KB）（586）       Knowledge map    Save Hydrogen production from photovoltaic (PV) power generation is an important approach to improving the consumption of renewable energy and reducing the impact on the power grid. To suppress power fluctuation from PV generation and loads in an electro-hydrogen system and reduce variations in the hydrogen storage state charge, and avoid frequent start-stop operations of hydrogen storage equipment caused by state-of-hydrogen limit violations, an electro-hydrogen coupled direct current (DC) microgrid cooperative control strategy is proposed. First, a fuzzy control algorithm is used to optimize and regulate the power allocation between hydrogen storage systems and lithium battery storage. Then, the DC microgrid operating conditions are divided into normal and extreme conditions according to the hydrogen storage state, and the hydrogen storage state of the hydrogen storage tank is dynamically adjusted by the proposed hydrogen storage variable-parameter sag control strategy to inhibit the speed of state of hydrogen of the hydrogen storage moving towards the overcharge and overdischarge intervals by different control strategies, and to improve the regulation capability of the DC microgrid. Finally, simulation analysis by MATLAB/Simulink verifies that the proposed control strategy can enhance the ability of the electro-hydrogen coupled DC microgrid to suppress source load fluctuations, maintain the stability of the DC bus voltage, and alleviate overcharge and overdischarge in the hydrogen storage energy systems, thus extending the service life of the equipment. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Dual-Time-Scale Optimal Scheduling for High-Energy-Consuming Industrial Park Considering Uncertainty of Photovoltaic WANG Jiaying, LU Chunguang, JIAO Wenshu, WU Qiuwei, CAO Yongji, YANG Jianli Journal of Shanghai Jiao Tong University    2026, 60 (6): 904-914.   DOI: 10.16183/j.cnki.jsjtu.2024.248 Abstract （1910）   HTML （2）    PDF（pc） （2486KB）（605）       Knowledge map    Save To fully exploit the potential regulation capacity of high-energy-consuming industrial loads and alleviate the pressure on the supply-demand balance of power systems caused by the large-scale access of renewable energy resources, this paper proposes a dual-time-scale optimal scheduling method for high-energy-consuming industrial parks considering the uncertainty of photovoltaic (PV). First, typical scenarios of PV power outputs are generated based on the conditional generation adversarial network to characterize the uncertainty of PV during the optimal scheduling process of industrial parks. Then, a dual-time-scale optimal scheduling model is established for high-energy-consuming industrial parks considering the coupling mechanism and regulation characteristics of various regulation resources in the industrial park. In this scheme, the production plan of high-energy-consuming industrial users is optimized to maximize the operation economy of the industrial park in the day-ahead stage, while power fluctuations of the industrial park are further mitigated by coordinating the tap position of electric arc furnaces and the charging/discharging power of energy storage resources in the intra-day stage. Finally, the optimal scheduling of an industrial park containing two typical high-energy-consuming industries, namely, iron and steel plants and cement plants is conducted to verify the effectiveness of the proposed method. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Optimization of Three-Degree-of-Freedom Biomimetic Pectoral Fin Propulsion Law Li Bin, Li Zonggang, Li Haoyu, Du Yajiang J Shanghai Jiaotong Univ Sci    2026, 31 (1): 195-208.   DOI: 10.1007/s12204-024-2579-5 Abstract （1878）      PDF（pc） （3693KB）（214）       Knowledge map    Save To optimize the movement of the three-degree-of-freedom (3-DOF) pectoral fins, a 3-DOF model of the dolphin-like pectoral fins was established, and the effects of different parameters of the pectoral fins on their propelling performance were simulated using computational fluid dynamics (CFD) technology. Using CFD simulation data as a training set and a multi-layer perceptron (MLP) neural network as a prediction model, the average thrust and lift of the pectoral fin motion under different motion cycles, rowing amplitudes, flapping amplitudes, and feathering amplitudes were predicted and modeled. A multi-objective genetic algorithm was used to obtain the optimal parameter values for maximum thrust and minimum absolute lift, and the optimal motion law for 3-DOF motion was brought. The results showed that the optimal propulsion performance was achieved at a period of 1 s, a rowing amplitude of 36 ◦ , a flapping amplitude of 18 ◦ , and a feathering amplitude of 56 ◦ . Finally, the force and displacement of the robotic fish were collected through indoor pool experiments and compared with the simulation results, indicating that the simulation results are of considerable reliability. The research results have specific guiding significance for the design of the pectoral fins of biomimetic robotic fish. Reference | Related Articles | Metrics | Comments（0）

Select

Transaction Mechanism of Medium- and Long-Term Continuous Centralized Auction Considering Available Transmission Capacity YANG Kaitao, LUO Xi, XUE Bike, GUO Yanmin, FU Xueqian Journal of Shanghai Jiao Tong University    2025, 59 (11): 1647-1659.   DOI: 10.16183/j.cnki.jsjtu.2023.628 Abstract （1780）   HTML （14）    PDF（pc） （2682KB）（698）       Knowledge map    Save With the advancement of the construction of the national unified electricity market system, the intra-provincial medium- and long-term electricity market adopts the trading mode of unrestricted clearance before security assessment, which is unable to meet the needs of the future medium and long-term market continuous market opening and high-frequency trading requirements. Therefore, this paper establishes a continuous centralized bidding trading mechanism and clearing model for the medium- and long-term electricity market. The continuous opening of the medium and long-term market is achieved through daily continuous trading, and the available transmission capacity is considered in the clearing model to improve the enforceability of trading results. Then, based on the marginal clearing price, it establishes a Marginal-Vickrey-Clarke-Groves mechanism to meet the balance of payments, which quantifies the contribution of market entities in the transaction through market efficiency coefficient, and distributes the market value, promoting the market participants to declare the true price information. Finally, based on medium- and long-term as well as spot electricity market trading data from a certain province in central China, a simulation analysis is conducted on the transaction data including three power generation enterprises and five power users. The clearing price is 399.64 yuan/(MW·h), which verifies the effectiveness and feasibility of the mechanism. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Low-Carbon Economic Dispatch of Energy Distribution Network in Synthetic Ammonia Park Considering Hydrogen Production by Water Electrolysis LI Haoran, SUN Hongbin, XUE Yixun, CHANG Xinyue, SU Jia Journal of Shanghai Jiao Tong University    2025, 59 (11): 1618-1624.   DOI: 10.16183/j.cnki.jsjtu.2024.010 Abstract （1762）   HTML （14）    PDF（pc） （965KB）（678）       Knowledge map    Save As a typical chemical industrial park, the coal to hydrogen ammonia synthesis park relies on fossil fuels such as coal, resulting in huge carbon emissions and difficulties in green and low-carbon transformation. As a clean and nonpolluting hydrogen production method, hydrogen production by water electrolysis has been developing quickly in recent years,which provides a probability for low-carbon operation of hydrogen to ammonia chemical industry parks. In this paper, a low-carbon operation framework of the energy distribution network in the synthetic ammonia park is proposed. Taking the traditional coal to hydrogen ammonia synthesis park as an example, the roof photovoltaic, electric energy storage, and hydrogen system are introduced to construct the coal-electricity-hydrogen-heat coupling energy distribution network in the chemical industry park based on coal to hydrogen and hydrogen to ammonia equipment, the original power system, and heating system, so as to realize the multi-energy complementary operation and reduce the carbon emission. Then, a model including hydrogen source, hydrogen load, energy storage system, and heat source is established. The economic dispatch model of the energy distribution network in the chemical industrial park is developed and solved to minimize the operation cost and carbon emission of the park and meet the operational constraints of the park. Finally, the case study shows that the proposed method can effectively reduce the operation cost and carbon emission of the park. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Self-Adaptive LSAC-PID Approach Based on Lyapunov Reward Shaping for Mobile Robots YU Xinyi, XU Siyu, FAN Yuehai, OU Linlin J Shanghai Jiaotong Univ Sci    2025, 30 (6): 1085-1102.   DOI: 10.1007/s12204-023-2631-x Abstract （1713）      PDF（pc） （2455KB）（531）       Knowledge map    Save In order to solve the control problem of multiple-input multiple-output (MIMO) systems in complex and variable control environments, a model-free adaptive LSAC-PID method based on deep reinforcement learning (RL) is proposed in this paper for automatic control of mobile robots. According to the environmental feedback, the RL agent of the upper controller outputs the optimal parameters to the lower MIMO PID controllers, which can realize the real-time PID optimal control. First, a model-free adaptive MIMO PID hybrid control strategy is presented to realize real-time optimal tuning of control parameters in terms of soft-actor-critic (SAC) algorithm, which is state-of-the-art RL algorithm. Second, in order to improve the RL convergence speed and the control performance, a Lyapunov-based reward shaping method for off-policy RL algorithm is designed, and a self-adaptive LSAC-PID tuning approach with Lyapunov-based reward is then determined. Through the policy evaluation and policy improvement of the soft policy iteration, the convergence and optimality of the proposed LSAC-PID algorithm are proved mathematically. Finally, based on the proposed reward shaping method, the reward function is designed to improve the system stability for the line-following robot. The simulation and experiment results show that the proposed adaptive LSAC-PID approach has good control performance such as fast convergence speed, high generalization and high real-time performance, and achieves real-time optimal tuning of MIMO PID parameters without the system model and control loop decoupling. Reference | Related Articles | Metrics | Comments（0）

Select

Gait Learning Reproduction for Quadruped Robots Based on Experience Evolution Proximal Policy Optimization LI Chunyang, ZHU Xiaoqing, RUAN Xiaogang, LIU Xinyuan, ZHANG Siyuan J Shanghai Jiaotong Univ Sci    2025, 30 (6): 1125-1133.   DOI: 10.1007/s12204-023-2666-z Abstract （1707）      PDF（pc） （1430KB）（334）       Knowledge map    Save Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic. The proximal policy optimization (PPO) algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity. To solve the problem, we propose a experience evolution proximal policy optimization (EEPPO) algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy. We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm. To verify the effectiveness of the proposed EEPPO algorithm, we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet. Experimental results show that the central pattern generator based radial basis function (CPG-RBF) network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed, posture and joints information. Experimental comparison results with the traditional soft actor-critic (SAC) algorithm validate the superiority of the proposed EEPPO algorithm, which can learn a more stable diagonal trot gait in flat terrain. Reference | Related Articles | Metrics | Comments（0）

Select

A Generative Digital Twin Modeling Method for Ship Thin-Plate Production Lines WEN Xiaojian, YAN Zhe, HU Zuozhi, YUAN Yi, BAO Jinsong, ZHANG Dan Journal of Shanghai Jiao Tong University    2026, 60 (8): 1279-1289.   DOI: 10.16183/j.cnki.jsjtu.2025.296 Abstract （1631）   HTML （2）    PDF（pc） （4353KB）（472）       Knowledge map    Save In shipbuilding, thin-plate production lines are characterized by complex processes, diverse equipment, and heterogeneous data sources. Traditional digital twin modeling relies heavily on manually defined interfaces and attributes, resulting in low efficiency and limited consistency. A generative digital twin modeling method tailored for thin-plate production lines is proposed. This method forms a unified semantic structure by constructing five core ontologies, covering process units, equipment, attributes, sensing-control units, and functional behaviors. Furthermore, a mapping mechanism from ontology to the digital twins definition language (DTDL) is designed to enable standardized representation of domain knowledge. A large language model (LLM) is then introduced to leverage its semantic understanding and generative capabilities, automatically transforming natural language descriptions into structured models compliant with the DTDL v3 specification. Semantic consistency validation is performed to ensure the correctness of model logic and constraints. Experimental results demonstrate that, compared with manual approaches, the proposed method improves modeling efficiency, semantic accuracy, and attribute coverage by approximately 79.6%, 9.4%, and 28.5%, respectively. This significantly enhances the automation and standardization of digital twin modeling. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Semantic Reconstruction Method of Digital Twin Model Based on Large Language Model BIAN Wenchao, WEN Xiaojian, WANG Xinhou, BAO Jinsong Journal of Shanghai Jiao Tong University    2026, 60 (8): 1290-1298.   DOI: 10.16183/j.cnki.jsjtu.2025.240 Abstract （1622）   HTML （5）    PDF（pc） （4461KB）（516）       Knowledge map    Save The rapid development of information technology and the deep advancement of Industry 4.0 are driving the widespread application of digital twin technology in the intelligent transformation of manufacturing. However, existing digital twin models in manufacturing still have limitations at the semantic level, as manifested in inadequate multidimensional attribute descriptions of physical assets, insufficient dynamic interaction capabilities, and lack of unified semantic standards and specifications. To address these issues, this paper proposes a semantic reconstruction method for digital twin models based on large language models (LLMs). This method utilizes a digital twin definition language to build a semantic information asset library and integrates large language models with knowledge graph technology to generate and enrich asset semantic information in real time, thereby enabling dynamic semantic reconstruction of digital twins. The experimental results focusing on a piston processing production line demonstrate that, compared to traditional manual modeling methods, the proposed semantic reconstruction method based on LLM significantly improves efficiency and stability in handling complex tasks, respond rapidly to dynamically changing production demands, and has advantages in visual presentation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Resilience Improvement Strategy of Distribution Network in Ice Disaster Considering Microgrids Coordination FANG Ziwen, ZHOU Yongzhi, DAN Yangqing, WEI wei Journal of Shanghai Jiao Tong University    2026, 60 (4): 584-594.   DOI: 10.16183/j.cnki.jsjtu.2024.117 Abstract （1622）   HTML （6）    PDF（pc） （1828KB）（1123）       Knowledge map    Save Line icing caused by ice disasters significantly affects the safety of distribution network. The advent of microgrid with energy storage provides a new method to enhance the resilience of the system. This paper proposes a resilience enhancement strategy for the coordinated operation of microgrids and distribution networks, fully leveraging the emergency power supply capabilities of distributed generation and energy storage systems of microgrids. First, considering the fault evolution characteristics of transmission line icing disasters, a quantitative risk assessment model for icing on distribution network lines is developed. Next, a rolling optimization approach based on a hybrid long-short time scale is proposed, in which the long-term optimization focuses on cross-day microgrid energy storage pre-dispatch and distribution line repair strategies, while the short-term day-ahead optimization mainly addresses intraday uncertainties caused by renewable energy variability and network topology changes induced by icing-related fault evolution, with scheduling strategies generated using a column-and-constraint generation algorithm. The long-and-short time-scale optimization models are executed in a rolling manner over the whole ice disaster period. Finally, the effectiveness of the proposed resilience enhancement strategy is validated through simulation studies on a modified IEEE standard 33-node distribution network. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

Select

Dynamic Modeling and Optimal Defense Strategy Against DdoS Attacks on Power Advanced Metering Infrastructure LIANG Haolan, LIU Dongqi, ZENG Xiangjun, ZHANG Qiong, ZHANG Tao, WANG Rui Journal of Shanghai Jiao Tong University    2025, 59 (11): 1660-1674.   DOI: 10.16183/j.cnki.jsjtu.2024.006 Abstract （1543）   HTML （10）    PDF（pc） （3580KB）（735）       Knowledge map    Save Advanced metering infrastructure (AMI) is a key component of new power systems. However, the wide application of heterogeneous communication networks and intelligent terminals makes it vulnerable to cyber-attacks. Therefore, this paper studies the dynamic modeling and optimal defending strategy of AMI network under distributed denial-of-service (DDoS) attacks. First, the propagation path of DDoS attack in AMI network is analyzed. Combined with the complex network theory and the SEIR epidemic model, a state evolution model is established to describe the state evolution of nodes after DDoS attack and the propagation mechanism and attack tolerance level of DDoS attack in AMI network are analyzed. Then, a defending strategy is proposed with the goal of minimizing defense losses and costs to flexibly optimize the deployment of defense resources. Finally, the effectiveness of the proposed strategy is verified by using a large number of numerical simulations in two different AMI network structures. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Prediction of Fatigue Crack Growth in Metal Materials via Spatiotemporal Neural Network LIANG Jiaming, YU Yin, HU Yile Journal of Shanghai Jiao Tong University    2026, 60 (3): 511-521.   DOI: 10.16183/j.cnki.jsjtu.2024.090 Abstract （1539）   HTML （28）    PDF（pc） （30162KB）（1444）       Knowledge map    Save An image-driven model based on spatiotemporal neural network (STNN) is proposed for prediction of crack growth in aluminum alloy. Fatigue experiments with an initial edge crack angle of 0° and a 15.0% limit load level are designed, and images of specimen deformation are captured using digital image correlation (DIC) resulting in 5 511 frames of displacement field data used as datasets of STNN after interpolation, augmentation, and dimension-raising. Two neural netwroks, convolutional long short-term memory (Conv-LSTM) and SimVP, are employed to predict the fatigue crack growth, with their prediction accuracies further compared based on the structural similarity index measure (SSIM) and the root mean square error (RMSE). The results show that the SimVP neural network performs better in the test stage predicting fatigue crack growth rate and propagation path. This method provides a reference for damage tolerance analysis and determination of inspection intervals for structures. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Analytical Solution for Vertical Vibration Response of Screw Piles Considering Three-Dimensional Wave Effect in Soil WANG Boyu, HU Zhiping, ZHANG Yonghui, YIN Ke, MA Jiakuan Journal of Shanghai Jiao Tong University    2025, 59 (12): 1855-1865.   DOI: 10.16183/j.cnki.jsjtu.2024.022 Abstract （1536）   HTML （12）    PDF（pc） （3452KB）（554）       Knowledge map    Save By modeling the soil as a three-dimensional axisymmetric medium and considering the three-dimensional wave effects within it, a theoretical study is conducted on the frequency domain characteristics of the longitudinal vibration of threaded piles in viscoelastic foundations. Based on the three-dimensional wave theory, the wave equation of the soil surrounding the pile under axisymmetric conditions is established. The vibration response solution of the screw pile under fully coupled pile soil conditions is obtained using Laplace transformation and variable separation methods. The results show that compared to the models that neglect radial displacement, the solution incorporating the three-dimensional wave effect of soil captures both longitudinal and shear waves in the soil, offering higher accuracy. In the low-frequency range, longer screw piles exhibit greater dynamic stiffness and damping, with the lateral friction along the pile shaft playing a more prominent role. Additionally, reducing the spacing between screw threads enhances the restraining effect of the surrounding soil, significantly improving vertical bearing capacity of the pile foundation. The existence of the screw threads has a substantial influence on the vibration of the screw pile. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Modification of Impedance Matrix and Short-Circuit Current Suppression in High-Proportion Wind Power Systems with Crowbar Activation LIU Shengsong, XU Yunqing, CHEN Dawei, CAO Yi, WANG Shengbo, LIU Houru Journal of Shanghai Jiao Tong University    2026, 60 (8): 1238-1246.   DOI: 10.16183/j.cnki.jsjtu.2024.516 Abstract （1531）   HTML （6）    PDF（pc） （1826KB）（483）       Knowledge map    Save The short-circuit current characteristics of wind power generation systems represented by doubly-fed induction generators (DFIGs) differ from those of synchronous generators. The inconsistency of DFIGs equivalent impedance before and after faults leads to inaccuracies in short-circuit current calculations for power systems with high wind power penetration. Therefore, a rapid calculation method and suppression strategy for large-scale grid short-circuit currents under high wind power penetration is proposed in this paper. First, the variation in equivalent impedance before and after faults is quantified, and the system node impedance matrix is modified by analyzing the dynamic response characteristics of the rotor side of DFIGs. Then, the fault current limiter (FCL) is equivalently modeled as a shunt impedance to avoid matrix inversion operations in the node impedance matrix. An iterative impedance unit configuration method is proposed to optimize the FCL placement and suppress the short-circuit currents. Simulation results demonstrate that the proposed method effectively suppresses short-circuit currents in wind-integrated systems. Compared with existing approaches, the proposed method requires fewer FCLs with lower capacity while achieving higher computational efficiency. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Data-Model Hybrid-Driven Simulation Method for Electric Vehicle Fleet Behavior LIU Lin, YANG Siyu, HUANG Xianan, CHEN Yantao, XU Huashuai, WANG Lingling, JIANG Chuanwen Journal of Shanghai Jiao Tong University    2026, 60 (1): 51-60.   DOI: 10.16183/j.cnki.jsjtu.2024.082 Abstract （1509）   HTML （23）    PDF（pc） （1875KB）（648）       Knowledge map    Save To address the issues of lack of actual electric vehicle (EV) charging load data, significant regional differences, and complex simulation methods, a behavior simulation method for EVs is proposed, which consists of a trip chain, an energy consumption chain, and a charging chain. First, to tackle the scarcity of charging data, a data-driven approach is adopted to derive the construction method and process of vehicle trip chain based on Gaussian mixture model (GMM). The travel patterns of EVs are obtained based on the similarities between EV and conventional vehicle trips. Then, energy consumption models are summarized, creating energy consumption chains based on trip chains. Finally, considering factors like charging anxiety, queueing, and charging time, the EV charging chain is developed. Simulations are conducted on common charging strategies to verify the impacts on user charging costs and the power grid. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Bi-Level Flexible Planning of Source-Grid-Load-Energy Storage Based on GAN MA Su, LIU Lu, CHENG Haozhong, ZHANG Xiaohu, XU Ling, LOU Wei Journal of Shanghai Jiao Tong University    2026, 60 (4): 531-540.   DOI: 10.16183/j.cnki.jsjtu.2024.214 Abstract （1481）   HTML （21）    PDF（pc） （2996KB）（591）       Knowledge map    Save To address the high penetration of renewable energy, it is necessary to advance the planning and operation of both renewable and coal-fired power, and to develop a source-grid-load-storage flexible planning method that considers uncertainty scenario generation. First, generative adversarial networks (GANs) are employed to generate seasonal (spring, summer, autumn, and winter) scenarios of wind and solar uncertainties. Based on a further analysis of renewable energy uncertainty and coal-fired power flexibility retrofit characteristics, a two-stage stochastic planning model with both long-term and short-term time scales is established, incorporating renewable energy and coal-fired power. This model explores flexible source-grid-load-storage planning schemes that account for renewable energy decisions and coal-fired power flexibility retrofit. Scenario accuracy indicators and insufficient flexibility indicators are used to investigate the correlation between uncertainty scenario generation methods and planning outcomes. Finally, the proposed method is validated via a practical case system. The proposed scheme exhibits a remarkable deep peak-regulation effect during severe noon peak-regulation periods in summer and autumn. It can accommodate large-scale renewable energy integration in the future and achieves superior economic performance for the source-grid-load-storage system. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Unsupervised Domain Adaptation for Cross-Regional Scenes Person Re-Identification MAO Yanmei, LI Huafeng, ZHANG Yafei Journal of Shanghai Jiao Tong University    2025, 59 (12): 1878-1890.   DOI: 10.16183/j.cnki.jsjtu.2023.635 Abstract （1469）   HTML （13）    PDF（pc） （13910KB）（1053）       Knowledge map    Save In large-scale surveillance systems, the lack of positive cross-camera pedestrian samples in cross-regional scenes limits the performance of person re-identification (Re-ID) models. To tackle this challenge, an unsupervised domain adaptive person re-identification method incorporating multi-granularity feature mining and domain-invariant feature learning is proposed. The method consists of a multi-granularity feature learning module and a domain distribution alignment module. Within the multi-granularity feature learning module, global discriminant features of pedestrians are extracted through global features learning. To further enhance the discriminative capability of pedestrian features, a local consistency feature learning module is proposed to strengthen the interactions among local features. By jointly learning global and local features, the network is encouraged to extract multi-granularity discriminative features, thereby improving the performance of the person re-identification model. Additionally, a domain distribution alignment module is incorporated, leveraging style transfer to generate positive samples with diverse styles across cameras for target domain. This not only addresses the issue of the lack of positive samples across cameras in cross-regional scenes but also enhances the domain adaptation capabilities of the model. Extensive experiments conducted on the Market-1501, DukeMTMC, CUHK03, and MSMT17 datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art person re-identification methods. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）