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Two-Stage Optimal Dispatch for Integrated Energy System with Oxy-Combustion Based on Multi-Energy Flexibility Constraints PENG Chuxuan, BIAN Xiaoyan, JIN Haixiang, LIN Shunfu, XU Bo, ZHAO Jian Journal of Shanghai Jiao Tong University    2025, 59 (9): 1281-1291.   DOI: 10.16183/j.cnki.jsjtu.2023.487 Abstract （1265）   HTML （5）    PDF（pc） （2004KB）（5305）       Knowledge map    Save As one of the most promising carbon capture technologies for coal-fired power plants, oxy-fuel combustion provides a new solution for improving the flexibility of the integrated energy system (IES) and reducing carbon emissions. In this paper, a two-stage optimal dispatch strategy for the integrated energy system with oxy-fuel combustion units considering the constraints of multi-energy flexibility is proposed based on the intergration of oxy-fuel combustion technology and the optimal operation of the integrated energy system. First, a model of integrated energy system with oxy-fuel combustion (Oxy-IES) is established. Then, a matrix model of multi-energy flexibility constraints for Oxy-IES is proposed to reveal the supply and demand relationship of flexibility within the system. Finally, a two-stage optimization dispatch strategy for Oxy-IES is constructed, in which the output of each unit is optimized to minimize the daily operating cost of carbon trading in the day-ahead stage, while the rapid variable load capacity of the oxy-fuel combustion unit improves the flexibility of the system in the intraday stage. The simulation results of Oxy-IES show that the proposed strategy can improve the flexibility and economy performance of the IES while reducing carbon emissions. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

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Renewable Energy Consumption Strategies of Power System Integrated with Electric Vehicle Clusters Based on Load Alignment and Deep Reinforcement Learning LIU Yanhang, QIAO Ruyu, LIANG Nan, CHEN Yu, YU Kai, WU Hanxiao Journal of Shanghai Jiao Tong University    2025, 59 (10): 1464-1475.   DOI: 10.16183/j.cnki.jsjtu.2023.529 Abstract （1338）   HTML （3）    PDF（pc） （3345KB）（3499）       Knowledge map    Save As China accelerates the construction of power systems with renewable energy as the mainstay, the large-scale integration of renewables has led to prominent issues such as wind and light curtailment. To improve the utilization of new energy consumption in power systems, this paper proposes a novel renewable energy consumption method based on load alignment and deep reinforcement learning. First, it proposes a node load line formation model based on linearized power flow calculations, which can guide adjustable loads to shift the electricity consumption period, thereby promoting the improvement of new energy consumption. Unlike the direct current (DC) power flow model, the proposed alternating current (AC) model accounts for voltage constraints and other related constraints of the power system. Compared with other AC power flow models, this model linearizes all nonlinear constraints and has lower computational costs. Then, this paper constructs a market framework for load alignment mechanism. The framework involves three main entities: independent system operators, regional power grid sellers, and electric vehicle adjustable load aggregators. It also explores the solution for load alignment incentive prices using electric vehicle clusters as adjustable loads. As the solution of the load benchmark incentive price involves a master-slave game between three entities, conventional mathematical analysis methods face high complexity. Therefore, it employs deep reinforcement learning algorithm to solve the problem. The deep reinforcement learning algorithm takes the marginal electricity price of each node as state space, the load benchmark incentive price as action space, and the cost of regional power grid sellers as feedback. The agent can find the load line incentive price that maximizes the benefits of regional power grid sellers after continuous training. Finally, the example analysis shows that the load alignment mechanism not only effectively promotes the improvement of new energy consumption level, but also enhances the interests of independent system operators, regional power grid sellers, and electric vehicle aggregators. The results further confirm that the deep reinforcement learning algorithm maximizes the benefits of regional power grid sellers. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun Journal of Shanghai Jiao Tong University    2025, 59 (6): 746-757.   DOI: 10.16183/j.cnki.jsjtu.2023.382 Abstract （2259）   HTML （11）    PDF（pc） （6089KB）（3321）       Knowledge map    Save The electric power industry plays a pivotal role in carbon emission control. Accurate and real-time accounting of carbon emissions in the power industry is essential for supporting the carbon reduction of the power industry. At present, the measurement of carbon emissions in the power industry relies mainly on direct measurement or the accounting methods, which often struggles to balance low measurement costs with real-time accuracy. Therefore, in this paper, the robust power data infrastructure in the power industry is leveraged and the correlation between electricity consumption and carbon emissions is explored to propose a short-term electricity-to-carbon method using machine learning methods based on historical data of electricity. This method utilizes convolutional neural networks (CNNs) for feature extraction, and light gradient boosting machine (LightGBM) for carbon emission estimation based on extracted features. Moreover, K-fold cross-validation is used in model training, with parameter optimization using grid search to enhance the generalization capability and robustness of the model. To validate the proposed method, it is compared with other machine learning models under the same data segmentation condition for daily and hourly data sets. The results indicate that the proposed model outperforms other models in both performance evaluation and the consistency between estimated and target values. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network CHEN Haolan, JIN Bingying, LIU Yadong, QIAN Qinglin, WANG Peng, CHEN Yanxia, YU Xijuan, YAN Yingjie Journal of Shanghai Jiao Tong University    2024, 58 (3): 295-303.   DOI: 10.16183/j.cnki.jsjtu.2022.091 Abstract （594）   HTML （14）    PDF（pc） （2938KB）（3213）       Knowledge map    Save To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Joint Policy Optimization of Quality Control, Condition-Based Maintenance and Spare Ordering for a Degradation System HAN Mengying, MA Shugang, YANG Jianhua, LI Wei, MA Zhichao Journal of Shanghai Jiao Tong University    2024, 58 (3): 361-370.   DOI: 10.16183/j.cnki.jsjtu.2022.356 Abstract （452）   HTML （11）    PDF（pc） （5697KB）（3180）       Knowledge map    Save A joint policy of quality control, condition-based maintenance and spare ordering is proposed for a degradation system subject to the delay time concept. First, considering the fact that product quality is largely dependent on system state, a two-stage inspection policy is proposed, in which the system state is detected during the initial deterioration process, but the product quality is checked after the defective state is found by an inspection. Then, based on the condition inspection information, quality information and failure information, the corresponding maintenance activity is chosen. Combining the state of the spare part when the system replacement is required, all possible events during an inspection interval are discussed and then a mathematical model of average cost rate is established. Afterwards, a simulation-based optimization approach coupling discrete event simulation and response surface methodology is devised to obtain a near optimal joint policy. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed policy by comparing it with the comparative policy. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Development of IT Industry in China in the New Age JIANG Ze-min Journal of Shanghai Jiaotong University    Abstract （18055）            Knowledge map    Save The article explains the role and future trend of IT industry, and states that information technology represented by the internet and computers has brought about the third industrial revolution in history. An important impetus for economic growth in modern times, the IT industry has greatly promoted sustainable development and is profoundly changing mankind’s way of life and production. In discussing the development trend of world IT industry, the article suggests that with potential new breakthroughs in IT technology, the trend of agglomeration and integration of industries has become increasingly obvious, competition of intellectual properties and strandards is intensifying and ubiquitous networks is taking shape. It points out that China should bring into better play the role of IT industry as an “amplifier” in economic growth, a “transformer” in development mode and a “propeller” in industrial upgrading. It is important to follow a policy that emphasizes indepandent innovation, marketdriven approach, open and compatible technologies, integrated and comprehensive application, and serving both military and civil purposes, so that a quantum leap of IT industry will be achieved. China should advance industrialization with IT technologles and promote the IT industry in the course of industrialization in an effort to build an IT industry with Chinese characteristics. Greater efforts should be made to develop such core sectors as microelectronics, computer, software, key components and materials, as well as sectors with international competitiveness, including broadband mobile communication, nextgeneration network and information services. Continued improvement should be made in the policies guiding the development of IT industry with a view to making China a country with a strong IT industry by 2020. Related Articles | Metrics | Comments（0）

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Transient Modeling and Characteristic Comparative Analysis of Grid-Forming VSC with and Without Current Control REN Xiancheng, LI Shangzhi, LI Yingbiao, HU Jiabing, XU Taishan, BAO Yanhong, WU Feng Journal of Shanghai Jiao Tong University    2025, 59 (7): 971-982.   DOI: 10.16183/j.cnki.jsjtu.2023.416 Abstract （1346）   HTML （8）    PDF（pc） （2357KB）（2502）       Knowledge map    Save As the support capacity of renewable energy generation equipment for the power grid needs enhancement, grid-forming control has attracted extensive attention, among which the virtual synchronous generator (VSG) has emerged as a key research frontier and is already being applied in engineering demonstration. Voltage source converter (VSC) with VSG as the synchronization link can be classified into voltage and current dual loop control and direct voltage control according to whether there is a current control loop in the structure. The difference in the two control structures has a significant impact on the transient characteristics of VSC. To study the difference between transient characteristics of two kinds of VSCs, the transient models are developed based on the “power excitation-internal voltage response” model, and the formation mechanism of internal voltage and transient characteristics are comparatively analyzed. Since the VSG simulates the operation characteristics of the synchronous machine, the equivalent inertia and equivalent damping of the VSC are analytically obtained at the electromechanical scale, and their transient behaviors are compared. It is found that the equivalent inertia and damping of a VSC with direct voltage control remain constant, while those of a VSC with voltage and current dual loop control exhibit time-varying characteristics and are numerically smaller than of the direct voltage control system. Finally, the validity of the theoretical analysis is confirmed by electromagnetic transient simulation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Detection of Roadside Vehicle Parking Violations Under Random Horizontal Camera Condition ZHAN Zehui, ZHONG Ming’en, YUAN Bingan, TAN Jiawei, FAN Kang Journal of Shanghai Jiao Tong University    2025, 59 (10): 1568-1580.   DOI: 10.16183/j.cnki.jsjtu.2023.578 Abstract （402）   HTML （1）    PDF（pc） （46166KB）（2477）       Knowledge map    Save Investigation and punishment of vehicle parking violations is important in urban traffic management. Considering the time-consuming and labor-intensive nature of manual law enforcement, as well as the limited scope of fixed camera monitoring and detecting, exploring more flexible and efficient automatic detection methods has a great practical significance. Thus, a cruise detection technology is proposed, which is suitable for mobile carriers requiring no stopping and can be completed in a single pass. First, a vehicle parking violation image dataset named XMUT-VPI is collected and constructed under the conditions of approximate horizontal views and random shooting angles, laying a data foundation for the research. Then, a multitask parking network (MTPN) is constructed as an encoder to extract the key element information required for stop violation judgment. With the aid of the self-designed deformable large kernel feature aggregation module (DLKA-C2f) and cross-task interaction attention mechanism (CTIAM), a highest average detection accuracy of 90.3%, a minimum average positioning error of 4.4%, and a suboptimal average segmentation intersection ratio accuracy of 78.5% are achieved. Finally, an efficient decoder is designed to further extract the skeleton features of the parking space line and fit the visible area of the main parking space, which helps match the target vehicle and analyzes the positional condition between its tire ground-touching points and the main parking space. In addition, a judgment principle is provided for three typical behaviors of illegal parking, improper parking, and standardized parking. Experimental results show that the algorithm attains a comprehensive accuracy rate of 98.1% for vehicle parking violation detections across diverse complex interference scenarios, which outperforms existing mainstream methods and can provide technical supports for fully automate road cruise management of parking violatic. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Review of Research on Condition Assessment of Nuclear Power Plant Equipment Based on Data-Driven XU Yong, CAI Yunze, SONG Lin Journal of Shanghai Jiao Tong University    2022, 56 (3): 267-278.   DOI: 10.16183/j.cnki.jsjtu.2021.502 Abstract （4674）   HTML （325）    PDF（pc） （1383KB）（2366）       Knowledge map    Save The condition assessment of the entire life cycle of nuclear power equipment has a significant impact on improving the safety and economy of nuclear power plants. In the past, operation and maintenance of systems, equipment, and structures of domestic nuclear power plants, mostly relied on the alarm mechanism of equipments, the simple threshold judgments of parameters, or the empirical judgments of engineers. With the implementation of online monitoring system in nuclear power plants, a large number of equipment operation data have been accumulated, and the use of data-driven technology to assess the health of equipment has become the focus of attention in the industry. In this paper, the current situation of the online monitoring system of nuclear power equipment was introduced and the common malfunction of nuclear power equipment was analyzed. The condition assessment of nuclear power equipment were categorized into three major problems (i.e., anomaly detection, life prediction, and fault diagnosis), the situation of research and application were summarized respectively, and the application potential of deep learning technology in this field was emphasized. Based on this, the challenges and possible solutions to the condition assessment of nuclear power plant equipment were further analyzed. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(4)

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Decision-Making Method of Intelligent Vehicles: A Survey HU Yikai, WANG Chunxiang, YANG Ming Journal of Shanghai Jiao Tong University    2021, 55 (8): 1035-1048.   DOI: 10.16183/j.cnki.jsjtu.2020.387 Abstract （1639）   HTML （40）    PDF（pc） （1687KB）（2086）       Knowledge map    Save Combined with the current research status of the intelligent vehicle decision-making methods at home and abroad, this paper classifies and summarizes decision-making methods from four aspects: decision input and output, environment interaction, and algorithm types. Besides, it analyzes their advantages and disadvantages, and evaluates applicable scenarios. Moreover, it surveyes the common data sets and current evaluation standards which are used for decision-making researches. Furthermore it discusses the technical difficulties faced by current decision-making methods and future development trends. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(1)

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Cascade Sliding Mode Decoupling Control of Coupled Inductor Single-Input Dual-Output Buck Converter Based on Super-Twisting Extend State Observer HUANG JinFeng, ZHANG Qian Journal of Shanghai Jiao Tong University    2025, 59 (5): 592-604.   DOI: 10.16183/j.cnki.jsjtu.2023.349 Abstract （1538）   HTML （2）    PDF（pc） （4220KB）（2073）       Knowledge map    Save To address the coupling effect between the output branches of the coupled inductor single-input dual-output (CI-SIDO) Buck converter, which leads to the cross-influence and thus affects the dynamic performance of the system, a cascaded sliding mode decoupling control strategy based on the super-twisting extend state observer (ST-ESO) is proposed. First, a state-space averaging model of the CI-SIDO Buck converter is established. Then, the coupling terms, internal perturbations, and unmodeled parts in the inner and outer loops of the converter are estimated by using the ST-ESO with a fast-convergence property, which are regarded as the total perturbations in the inner and outer loops. Next, the total perturbation in the inner and outer loops is compensated by using a super-twisting sliding mode controller to achieve the decoupling of the system and ensure the robustness of the system and the stability of the output voltage. Furthermore, the stability of the super-twisting extend state observer and super-twisting sliding mode controller is analyzed using the Lyapunov theory, providing theoretical verification of the feasibility of the control strategy. Finally, the proposed control strategy is experimentally validated on the experimental platform. The results show that the proposed control strategy realizes the decoupling of the system, suppresses the cross-influence and improves the dynamic performance of the system. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Online Steady-State Scheduling of New Power Systems Based on Hierarchical Reinforcement Learning ZHAO Yingying, QIU Yue, ZHU Tianchen, LI Fan, SU Yun, TAI Zhenying, SUN Qingyun, FAN Hang Journal of Shanghai Jiao Tong University    2025, 59 (3): 400-412.   DOI: 10.16183/j.cnki.jsjtu.2023.344 Abstract （1801）   HTML （8）    PDF（pc） （4192KB）（2015）       Knowledge map    Save With the construction of new power systems, the stochasticity of high-proportion renewable energy significantly increases the uncertainty in the operation of the power grid, posing severe challenges to its safe, stable, and economically efficient operation. Data-driven artificial intelligence methods, such as deep reinforcement learning, are becoming increasingly important for regulating and assisting decision-making in the power grid in the new power system. However, current online scheduling algorithms based on deep reinforcement learning still face challenges in modeling the high-dimensional decision space and optimizing scheduling strategies, resulting in low model search efficiency and slow convergence. Therefore, a novel online steady-state scheduling method is proposed for the new power system based on hierarchical reinforcement learning, which reduces the decision space by adaptively selecting key nodes for adjustment. In addition, a state context-aware module based on gated recurrent units is introduced to model the high-dimensional environmental state, and a model with the optimization objectives of comprehensive operating costs, energy consumption, and over-limit conditions is constructed considering various operational constraints. The effectiveness of the proposed algorithm is thoroughly validated through experiments on three standard test cases, including IEEE-118, L2RPN-WCCI-2022, and SG-126. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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User Perception Modeling by Combining Structural Equation Model and Artificial Neural Network YAN Bo,CHU Xuening,ZHANG Lei Journal of Shanghai Jiaotong University    2019, 53 (7): 830-837.   DOI: 10.16183/j.cnki.jsjtu.2019.07.009 Abstract （3086）      PDF（pc） （1420KB）（1996）       Knowledge map    Save It is difficult for the existing research methods to describe the nonlinear relationship and influence path among the users’ multiple perception constructs during the product usage. This may lead that the user perception model is not real and accurate enough. Therefore, a new method combining structural equation model (SEM) with artificial neural network (ANN) is proposed for user perception modeling. Firstly, based on the results of SEM analysis, main factors that influence user perception and the causal relationship between user perception constructs are identified; Then, the result of SEM analysis is converted to the topology of the ANN model, so that a structured artificial neural network model for user perception is established, in order to get the connection weights between the network nodes the BP (back propagation) algorithm is used to train the model; Finally, the validity of the proposed method is demonstrated by a case study of smart phone user perception modeling, the results show that the SEM-ANN model with good goodness of fit and interpretability can more accurately and quantitatively express the relationship between user perception constructs and the factors that influence user perception constructs. Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(5)

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Dynamic Equivalence Modeling of Short-Circuit Faults in Wind Farms Considering Wake Effects YU Hao, LI Canbing, YE Zhiliang, PENG Sui, REN Wanxin, CHEN Sijie, TANG Binwei, CHEN Dawei Journal of Shanghai Jiao Tong University    2024, 58 (6): 798-805.   DOI: 10.16183/j.cnki.jsjtu.2022.476 Abstract （1458）   HTML （12）    PDF（pc） （2071KB）（1917）       Knowledge map    Save Fast and accurate analysis of the short-circuit characteristics of large wind farms has important engineering application value, and the short-circuit characteristics of wind farms under the influence of the wake effect vary greatly. Therefore, it is necessary to establish a wind farm short-circuit fault time equivalence model. A wind farm short-circuit fault dynamic equivalence method considering the effect of wake effect is proposed. First, the wake effect factor is defined to reflect the degree of the unit affected by the wake effect. Then, the wake effect factor is used as the grouping basis to reduce the variability of operating state of the units within the group under the influence of the wake effect. A positive- negative- zero-sequence network equivalence method is analyzed to improve the effectiveness of the equivalence model in asymmetric short-circuit faults. An equivalence method suitable for zero-sequence network is proposed and a platform is built for verification. The simulation results show that the dynamic short-circuit fault equivalence model proposed can accurately reflect the active and reactive short-circuit output characteristics of wind farms under the influence of the wake effect. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(1)

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Capacity Planning and Operational Optimization for Low-Carbon Data Center Integrated Energy System Considering Exergy Efficiency LIN Jiayu, HAN Juntao, WANG Yongzhen, HAN Kai, HAN Yibo, LI Jian Journal of Shanghai Jiao Tong University    2025, 59 (9): 1327-1337.   DOI: 10.16183/j.cnki.jsjtu.2023.528 Abstract （1583）   HTML （3）    PDF（pc） （3820KB）（1874）       Knowledge map    Save With the rapid development of the digital economy, the energy consumption and carbon emissions of data centers (DCs) have significantly increased. In recent years, the construction of data center integrated energy systems (DC-IES) has emerged as one of the critical trends in energy conservation and emission reduction for DCs under the global net-zero emission initiative. To support the planning and construction of low-carbon DC-IES, this paper proposes a multi-objective optimization model for capacity allocation and operational planning of DC-IES, integrating energy and economic considerations with a focus on low-carbon performance. Based on the “quality” analysis method of exergy from the second law of thermodynamics, the model proposed comprehensively accounts for the dynamic exergy efficient characteristics of energy conversion devices under varying load conditions, revealing the energy flow distribution characteristics of DC-IES under different objectives. The computational results indicate that compared with the optimization scheme assuming constant equipment efficiency, the scheme considering dynamic equipment efficiency reduces energy loss rate, economic cost, and carbon emissions by 2.6%, 1.9%, and 4.8%, respectively, demonstrating clear advantages. Moreover, compared with the economically optimal scheme, the multi-objective optimization scheme significantly reduces carbon emissions and energy loss rate of the DC-IES by 22.72% and 20.73%, respectively. Furthermore, compared to the scheme scenarios with the minimum exergy loss rate and lowest carbon emissions, the multi-objective optimization scheme reduces economic costs by 54.54% and 60.78%, respectively. Compared with the scheme relying solely on grid electricity supply, the multi-objective optimization scheme that regards the DC as an integrated energy system can reduce carbon emissions by 40.97%. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

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Application of an Air Source Heat Pump System with a Three-Cylinder Two-Stage Variable Volumn Ratio Rotary Compressor XIAO Biao, YAN Yan, ZHAO Shunan, HUANG Tongyi, LI Xiang Journal of Shanghai Jiao Tong University    2021, 55 (2): 188-195.   DOI: 10.16183/j.cnki.jsjtu.2019.269 Abstract （1290）   HTML （6）    PDF（pc） （1782KB）（1824）       Knowledge map    Save Aimed at the problem of the compressors of the low temperature air source heat pump system, this paper analyzes the impact of volume ratio on performance and proposes a novel three-cylinder two-stage variable volume ratio rotary compressor. The performance of the proposed compression system is compared with that of the traditional two-stage compression system of the same terminal in the experiments. The results show that the three-cylinder two-stage system operates in a stable manner with a coefficient of performance (COP) of 1.52 at a ambient temperature of -30 ℃,while the traditional two-stage system does not work. The COP of the three-cylinder two-stage system is always 1.25% to 12.41% higher than that of the traditional two-stage systems at any ambient temperature. When the ambient temperature is stable and the water supply temperature increases, the amount of dissipated heat at the terminal increases. At the same time, the maximum heat of external machine decreases, as well as the COP. When the ambient temperature is 7 ℃ and -25 ℃ respectively, and the water supply temperature changes from 40 ℃ to 55 ℃,the COP of the three-cylinder two-stage system is 1.15% to 8.86% and 4.32% to 7.33% higher than that of the traditional two-stage system, respectively. The power consumption of the three-cylinder two-stage system is always 3.78% to 16.67% lower than that of the traditional two-stage system. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(1)

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Reactive Power-Voltage Droop Gain Online Tuning Method of Photovoltaic Inverters for Improvement of Stable Output Power Capability in Weak Grids WANG Yuyang, ZHANG Chen, ZHANG Yu, WANG Yiming, XU Po, CAI Xu Journal of Shanghai Jiao Tong University    2025, 59 (6): 845-856.   DOI: 10.16183/j.cnki.jsjtu.2023.353 Abstract （1902）   HTML （5）    PDF（pc） （7800KB）（1823）       Knowledge map    Save The active power output capability and small signal stability in weak grids are key factors that limit stable photovoltaic (PV) power generation. To improve stably generating PV power in weak grids, an adaptive control method for PV inverters based on online tuning of the reactive power-voltage (Q-V) droop gain is proposed. First, to ensure active power output capability in weak grids, a “first optimization” method for the Q-V droop gain is proposed, considering voltage and current constraints. Then, to address stability constraints in weak grids, impedance modeling and stability analysis of the PV inverter system are conducted. A mapping relationship between the “parameter-weakest pole” is established with the weakest pole of the closed-loop system as a stability constraint based on the artificial neural network. A “second adjustment” method for the Q-V droop gain is developed at stably generating active power. Combined with the extended Kalman-filter-based grid impedance estimation, the proposed Q-V droop gain adaptive tuning method is realized. The effectiveness of the proposed adaptive control method is validated on the Modeling Tech real-time simulation platform. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

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Power System Transient Stability Assessment Based on Extreme Learning Machine ZHANG Linlin,HU Xiongwei,LI Peng,SHI Fang,YU Zhihong Journal of Shanghai Jiaotong University    2019, 53 (6): 749-756.   DOI: 10.16183/j.cnki.jsjtu.2019.06.017 Abstract （1256）      PDF（pc） （1033KB）（1807）       Knowledge map    Save With the enhanced trend of alternative clean energy and power electronics in power system, the traditional numerical simulation methods based on theoretical model will face new challenges, while the self-adaptive data-driven power system stability assessment method is gaining more and more attention. Based on the theory of extreme learning machine (ELM), a transient stability assessment method suitable for online application is proposed. Firstly, the samples are screened by adjusting the ratio of stable and unstable simulation samples, to reduce the sample imbalance in which unstable samples are far less than stable ones in the sample set, and the recursive feature elimination is used to further process the sample set. Then, the cross-validation is used to optimize the ELM network structure, and the processed sample set is used to train the ELM. Finally, the system stability based on the output of the neural network is predicted, and the reliability of the results with the improved evaluation criteria is evaluated. Test results show that the recursive feature elimination can significantly reduce the feature redundancy and improve the performance of the model, and the proposed algorithm has a shorter training time while can provide more accurate results. Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(5)

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Modeling of Multi-Modal Knowledge Graph for Assembly Process of Wind Turbines with Multi-Source Heterogeneous Data HU Zhiqiang, LIU Mingfei, LI Qi, LI Xinyu, BAO Jinsong Journal of Shanghai Jiao Tong University    2024, 58 (8): 1249-1263.   DOI: 10.16183/j.cnki.jsjtu.2023.062 Abstract （978）   HTML （23）    PDF（pc） （11842KB）（1760）       Knowledge map    Save The assembly process information of wind turbines is usually scattered in process documents consisting of multi-modal information, such as 3D models, natural texts, and images. Therefore, the cost of maintaining data and extracting process knowledge is high while the efficiency is low. To solve this problem, a multi-modal knowledge graph-based modeling method for the assembly process knowledge of wind turbines is proposed with multi-source heterogeneous data. First, the concepts in multi-modal process knowledge graph of wind turbine (MPKG-WT) are defined by analyzing the process characteristics of wind turbines to complete the construction of ontology. Then, based on the characteristics of multi-source heterogeneous data and multi-modal information, data analysis, knowledge extraction, and semantic similarity calculation are leveraged to realize the automatic instantiation of the graph. Finally, taking the process data of a wind turbine enterprise as an example, MPKG-WT is constructed and verified by implementing an auxiliary system for process design. The results show that MPKG-WT is more informative than the single-modal graph, and the data in different modals can complement each other, which leads to significant improvements in the efficiency of process design. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Dynamics Modeling and Validation of Coaxial Lifting Rotors HU Jinshuo, HUANG Jianzhe Journal of Shanghai Jiao Tong University    2022, 56 (3): 395-402.   DOI: 10.16183/j.cnki.jsjtu.2021.044 Abstract （4740）   HTML （116）    PDF（pc） （2567KB）（1711）       Knowledge map    Save The dynamics model for coaxial lifting rotors can be used to study the controller design and flight simulation for coaxial-rotor aerial vehicles. However, both the computational efficiency and the accuracy should be considered. First, the computational model of the induced velocity of lifting rotor everywhere including the wake region is derived based on adjoint theorem. Then, the finite state dynamics model for coaxial lifting rotors with wake skew considered is developed by extending the finite state inflow model for single rotor. Finally, the equations for calculating the thrust of coaxial lifting rotors in the hover condition are given, and the test is conducted. The results show that the computational complexity of the proposed dynamics model for coaxial lifting rotors is acceptable, and the computational thrusts are almost close to the test results when the rotational speed is within a certain range, which can also reflect the trend of the thrust lost for coaxial lifting rotors. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(3)

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A Review of Coupled Electricity and Hydrogen Energy System with Transportation System Under the Background of Large-Scale New Energy Vehicles Access LI Jiaqi, XU Xiaoyuan, Yan Zheng Journal of Shanghai Jiao Tong University    2022, 56 (3): 253-266.   DOI: 10.16183/j.cnki.jsjtu.2021.464 Abstract （5082）   HTML （972）    PDF（pc） （2106KB）（1675）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(14)

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Depth Distribution Characteristics of Particle Velocity Field Intensity in Shallow Sea ZHANG Haigang, XIE Jinhuai, LIU Jiaqi, GONG Lijia, LI Zhi Journal of Shanghai Jiao Tong University    2024, 58 (7): 995-1005.   DOI: 10.16183/j.cnki.jsjtu.2023.073 Abstract （1794）   HTML （9）    PDF（pc） （6512KB）（1639）       Knowledge map    Save The depth distribution characteristics of particle velocity field intensity have had a significant impact on underwater acoustic detection and estimation. In this paper, based on the approximate conditions of the incoherent normal modes sum transformation to angular integration, the angular integration form of incoherent normal modes of particle velocity was derived, which avoided the complex calculations of eigenvalues and eigenfunctions while revealing the physical mechanism behind the significant variations in particle velocity intensity with source depth and symmetrical depth. The numerical results demonstrate that the analytical expression of the angular integration of incoherent particle velocity can facilitate fast computation and effectively characterize the depth distribution characteristics of particle velocity intensity. Additionally, due to the superposition effect of the amplitude function of normal modes, there are notable differences in the depth distribution of vertical and horizontal particle velocity. Subsequently, focusing on the intensity difference of particle velocity, the study analyzed the effects of parameters such as horizontal distance, source frequency, sound speed profile, and water depth on the depth distribution characteristics of particle velocity field intensity. The findings provide a theoretical basis for passive target depth estimation based on vector fields. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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An Identification Method for DC-Link Capacitor Capacitance of Grid Connected Inverter ZHU Chenghao, WANG Han, SUN Guoqi, WEI Xiaobin, WANG Fuwen, CAI Xu Journal of Shanghai Jiao Tong University    2022, 56 (6): 693-700.   DOI: 10.16183/j.cnki.jsjtu.2021.515 Abstract （1461）   HTML （1286）    PDF（pc） （5050KB）（1580）       Knowledge map    Save DC-link for the capacitor is one of the most vulnerable components of the grid connected converter, whose capacitance identification will help to improve the system reliability by finding and replacing the aging capacitor in time. An identification method for the DC-link capacitor capacitance of the grid connected inverter based on pre-charging circuit is proposed. By analyzing the relationship between the capacitance and the charging current, charging voltage during pre-charging process, and combining the historical operating data, the set of capacitance state feature vector is built. The support vector regression (SVR) model is trained and the regression prediction relationship between the state value and the capacitance is set. The model is optimized by using the particle swarm optimization (PSO) algorithm, which can be used for capacitance identification of the DC-link capacitor. Simulation and experiments results show that the proposed method can implement the accurate capacitance identification of the DC-link capacitor of the grid connected inverter, with an identification error of less than 0.95%. This method does not need to add hardware circuit and change the control algorithm, and has a certain practical value. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(1)

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Control Strategy for Improving Active Frequency Support Capability of Offshore Wind Farm LI Yibo, ZHOU Qian, ZHU Dandan, JIANG Yafeng, WU Qiuwei, CHEN Jian Journal of Shanghai Jiao Tong University    2025, 59 (10): 1442-1450.   DOI: 10.16183/j.cnki.jsjtu.2023.581 Abstract （1576）   HTML （3）    PDF（pc） （1522KB）（1553）       Knowledge map    Save In low frequency alternating current (AC) transmission systems, offshore wind farm is unable to respond to changes in onshore grid frequency in a timely manner due to frequency decoupling and signal transmission delays between the offshore wind power system and the onshore AC system. To address this issue, a control strategy is proposed to improve the active frequency support capability of offshore wind farms by combining the system inertia. In terms of frequency signaling, an additional frequency sag controller is designed based on the V/f control strategy of the low-frequency-side structure network of modular multilevel matrix converter (M3C), combining with the system inertia. The frequency coupling link between the M3C net side and the low-frequency side is established to realize the real-time transmission of frequency information between the two sides. In terms of frequency support, when the system is disturbed to generate frequency deviation, the offshore wind turbine can adjust the power command value through additional droop control, thereby providing frequency support for the system. Finally, the effectiveness of the proposed coordinated control strategy is verified in MATLAB/Simulink by the simulation of load change and three-phase AC short circuit fault. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Challenges of Distributed Green Energy Carbon Trading Mechanism and Carbon Data Management LI Xingzhi, HAN Bei, LI Guojie, WANG Keyou, XU Jin Journal of Shanghai Jiao Tong University    2022, 56 (8): 977-993.   DOI: 10.16183/j.cnki.jsjtu.2021.450 Abstract （1558）   HTML （902）    PDF（pc） （2604KB）（1553）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(8)

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Distributed Photovoltaic Net Load Forecasting in New Energy Power Systems LIAO Qishu, HU Weihao, CAO Di, HUANG Qi, CHEN Zhe Journal of Shanghai Jiao Tong University    2021, 55 (12): 1520-1531.   DOI: 10.16183/j.cnki.jsjtu.2021.244 Abstract （2384）   HTML （244）    PDF（pc） （69168KB）（1510）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(8)

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Three-Dimensional Numerical Analysis of Soil Nailing for Deep Excavation DING Yong-chun, ZHOU Shun-xin, WANG Jian-hua Journal of Shanghai Jiao Tong University    2011, 45 (04): 547-552.   DOI: 10.16183/j.cnki.jsjtu.2011.04.019 Abstract （5502）   HTML （428）    PDF（pc） （1058KB）（1506）       Knowledge map    Save In order to investigate the deformation characteristics and load transfer mechanism of deep excavation, a numerical model considering the process of stepped excavating and soil nailing was established by FLAC 3D. The lateral displacement of excavation surface and outside soil, the shear strain increment of soil, and the axial force distribution of nail were analyzed. The results show that, the maximum lateral displacement appears close to slope toe, meanwhile, outside corner is the most disadvantageous location of deflection, and the multilevel platforms have positive effects on controlling soil deformation. Tensile failure and shear failure are the two main failure modes of excavation slope. The location of potential sliding surface can be indicated by shear strain increment contour, and the location moves toward the outside end of soil nail due to soil nail reinforcement effects. The maximum axial force of soil nail appears near the middle and the relative minimum value near the two ends, and the distribution of maximum axial force agrees well with the potential sliding surface. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Multi-Objective Optimization Strategy of Trajectory Planning for Unmanned Aerial Vehicles Considering Constraints of Safe Flight Corridors HUANG Yuhao, HAN Chao, ZHAO Minghui, DU Qiankun, WANG Shigang Journal of Shanghai Jiao Tong University    2022, 56 (8): 1024-1033.   DOI: 10.16183/j.cnki.jsjtu.2021.154 Abstract （1547）   HTML （47）    PDF（pc） （5684KB）（1493）       Knowledge map    Save Aimed at the problem of generating a smooth, safe, and dynamically feasible continuous-time trajectory for unmanned aerial vehicles (UAV) in complex environments, a trajectory planning algorithm is proposed to minimize a multi-objective function based on safe flight corridors. The safe flight corridor represented by a collection of convex polyhedra is built based on the initial discrete waypoints generated by the improved rapidly-exploring random tree(RRT), namely the RRT* algorithm. The safety objective function is established according to the constraints of limiting the trajectory inside safe flight corridors. In combination with the flight smoothness, dynamic characteristics, and time performance, a multi-objective function is built. The gradient-based convex optimization algorithm is used to derive the continuous-time trajectory expressed as a piece-wise polynomial by optimizing the position, velocity, acceleration of waypoints, and time allocation. The effectiveness and performance of the proposed algorithm is tested and compared under complex environments such as the coal mine. The test results demonstrate that the proposed algorithm has a better comprehensive performance in comparison with existing algorithms. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(2)

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Modeling and Stability Analysis of a Pressure Compensator for Flow-Control Valve JIA Wen-Hua- , YIN Chen-Bo Journal of Shanghai Jiaotong University    2011, 45 (04): 561-564.   Abstract （5874）            Knowledge map    Save The forces acting on the load-sensing compensated valve were illustrated and analyzed, the mathematical model was generated using classical momentum principles, while the linear analysis of this was carried out using the Taylor expansions. The valve motion of the system was described by presenting the equation of motion, the stability condition was given by Routh-Hurwitz stability criterion and the dynamic properties of the compensator were studied. The CFD analysis of the compensator was described to study the effect of the force acting on the bottom of the compensator. The pressure along the bottom wall was noted and its variation was plotted against the geometrical position of the bottom wall that it acted on. Noted that this pressure remain constant along the wall. Then, some plots describe the flow induced forces and coefficients used to understand the stability of the compensator itself. Reference | Related Articles | Metrics | Comments（0）

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Development Pathway of China’s Clean Electricity Under Carbon Peaking and Carbon Neutrality Goals HUANG Qiang, GUO Yi, JIANG Jianhua, MING Bo Journal of Shanghai Jiao Tong University    2021, 55 (12): 1499-1509.   DOI: 10.16183/j.cnki.jsjtu.2021.272 Abstract （2391）   HTML （532）    PDF（pc） （1849KB）（1432）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(36)

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Mechanism of Forced Subsynchronous Oscillation of Large-Scale Photovoltaic Power Generation Grid-Connected System with Series Compensation Tranmmission Lines LIN Yong, KANG Jiale, YU Hao, CHEN Honglin, YANG Yanji, CHEN Wuhui Journal of Shanghai Jiao Tong University    2022, 56 (9): 1118-1127.   DOI: 10.16183/j.cnki.jsjtu.2021.415 Abstract （1120）   HTML （135）    PDF（pc） （1463KB）（1415）       Knowledge map    Save There exists the subsynchronous oscillation (SSO) instability risk in large-scale photovoltaic(PV) grid-connected systems with series compensation, which is generally explained by the negative damped oscillation theory. In this paper, the inter-photovoltaic harmonics due to maximum power point tracking (MPPT) control are used as the disturbance source and the large-scale PV grid-connected system with series compensation as the forced system. The forced oscillation theory is used to reveal the SSO mechanism of PV power generation based on the interaction between the perturbed MPPT and the series compensation grid-connected system, and verified in the PSCAD/EMTDC simulation platform. The results show that the perturbed MPPT-based PV inverter outputs interharmonic currents to the system due to the modulation coupling on the AC-DC side, which may lead to serious forced SSO problems when the interharmonic frequency is close to the frequency of inherent weakly damped mode of the system, causing a shock to the system stability. The simulation results verify the correctness of the proposed theory. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(6)

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A Novel Weather Classification Method and Its Application in Photovoltaic Power Prediction LI Fen, ZHOU Erchang, SUN Gaiping, BAI Yongqing, TONG Li, LIU Bangyin, ZHAO Jinbin Journal of Shanghai Jiao Tong University    2021, 55 (12): 1510-1519.   DOI: 10.16183/j.cnki.jsjtu.2021.264 Abstract （1647）   HTML （147）    PDF（pc） （1327KB）（1412）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(7)

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Coupled Consolidation in Unsaturated Soils Based on Random Field Theory CHENG Yan, ZHANG Lu-lu, ZHANG Lei, WANG Jian-hua Journal of Shanghai Jiao Tong University    2014, 48 (11): 1528-1535.   DOI: 10.16183/j.cnki.jsjtu.2014.11.005 Abstract （962）   HTML （204）    PDF（pc） （397KB）（1412）       Knowledge map    Save Based on the random field theory, a random finite element model of coupled unsaturated consolidation in spatial random soils was established using the covariance matrix decomposition method and Monte Carlo simulation method. The effects of spatial variability of Young's modulus and saturated permeability were investigated. The results show that the mean and standard deviation of the settlement are significantly affected by the coefficient of variation of Young's modulus, and COVE. When COVE increases, the mean and standard deviation of the settlement increase, which means the uncertainty of the settlement are increased due to the spatial variability of Young's modulus. When the correlation length of Young's modulus δlnE decreases, the standard deviation of excess pore water pressure and the surface settlement decrease due to the increased spatial averaging effect. The spatial averaging effect is more significant when the thickness of soil layer is smaller than δlnE. In general, the effect of spatial variability of ks is much less significant than that of elastic modulus, which mainly affects the unsaturated consolidation in process. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Numerical Study of Combustion Characteristics of Methane/Hydrogen Hybrid Fuel of Lean Premixed Swirl WANG Xinci, LIU Aiguo, WU Xiaoqu, ZHANG Yunjie Journal of Shanghai Jiao Tong University    2024, 58 (8): 1179-1187.   DOI: 10.16183/j.cnki.jsjtu.2022.502 Abstract （557）   HTML （19）    PDF（pc） （22873KB）（1390）       Knowledge map    Save The numerical simulation method is used to study the influence of the mixing ratio of methane/hydrogen mixture on the combustion characteristics and pollutant emission characteristics of the combustor. The results show that due to the action of combustion chemical reaction, there exists a certain difference in the structure of cold and hot flow fields. The flow velocity of the hot flow field increases, and the recirculation zone becomes larger. The hydrogen content has a significant impact on the structure and temperature distribution characteristics of the hot flow field, and a central recirculation zone is formed when the hydrogen content is less than 20%, which can maintain stable combustion. When the hydrogen content is greater than 40%, the central recirculation zone disappears, the external recirculation zone is extended, and the spontaneous combustion and flashback occur to varying degrees. As the inlet air temperature increases, the spontaneous ignition phenomenon becomes more obvious, and the inlet air temperature decreases, the flashback phenomenon becomes more obvious. NOx emissions increase with the increase of hydrogen content, CO emissions decrease with the increase of hydrogen content, and CO is concentrated in the combustion zone of the main combustion stage. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Elastified Procedure for the Formfound Equilibrium State  and Zero-stress State of Cable-net Structure CHEN Wu-Jun, ZHANG Li-Mei, DONG Shi-Lin Journal of Shanghai Jiaotong University    2011, 45 (04): 523-527.   Abstract （5361）            Knowledge map    Save Six states and five analysis processes were pointed out for the whole numerical analysis process of cable-net structure. The six states are initial geometry state, form-found equilibrium state, elastified form-found equilibrium state, zero-stress (unstressed) state, pre-stress state and loading state, respectively. The five analysis process are form-finding analysis, elastified cable analysis, pre-stress analysis, inverse analysis and loading analysis. And the state parameters were clarified, also for the analytical model of different analysis procedures. The real zero-stress state does not exist generally, the assumed approximated zero-stress state is usually employed. On the basis of elastified form-found equilibrium state, the bestfit reasonable zero-stress state can be found through inverse analysis. The numerical algorithm and procedure was proposed for the ANSYS and EASY for cable-net analysis, and the emphasis is focused on the zero-stress state assumption. Finally, the numerical analysis was exemplified for two cablenet structures. The results demonstrate the significant difference with different zero-stress state assumption and the corresponding numerical analysis approaches. Reference | Related Articles | Metrics | Comments（0）

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Online State of Charge Estimation for Battery in Electric Vehicles Based on Forgetting Factor Recursive Least Squares CHEN Yushan, QIN Linlin, WU Gang, MAO Junxin Journal of Shanghai Jiao Tong University    2020, 54 (12): 1340-1346.   DOI: 10.16183/j.cnki.jsjtu.2020.172 Abstract （1598）   HTML （11）    PDF（pc） （1323KB）（1375）       Knowledge map    Save An advanced battery management system ensures the safe and efficient use of batteries in electric vehicles. As the state of charge (SOC) cannot be measured directly, it is important for the battery management system to accurately and reliably estimate the SOC of batteries. In order to estimate SOC, a first-order resistor-capacitance (RC) equivalent circuit model is used to describe the external characteristic of batteries. The model parameters are identified by forgetting factor recursive least-squares (FFRLS). Open circuit voltage (OCV) is one of the model parameters, and then SOC can be estimated by the SOC-OCV model. The CALCE battery research group in the University of Maryland has proposed some data, which include the data of LNMC/graphite battery working under dynamic stress test (DST) and Beijing dynamic stress test (BJDST) conditions. These data are used to verify the proposed algorithm. The results show that the estimation error does not exceed 3.419 0% in DST and 4.233 5% in BJDST, which indicates that the proposed method can realize online SOC estimation. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(4)

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Reconstruction of Ship Propeller Wake Field Based on Physics-Informed Neural Networks HOU Xianrui, ZHOU Xingyu, HUANG Xiaocheng Journal of Shanghai Jiao Tong University    2024, 58 (11): 1654-1664.   DOI: 10.16183/j.cnki.jsjtu.2023.101 Abstract （964）   HTML （18）    PDF（pc） （18611KB）（1366）       Knowledge map    Save Physics-informed neural networks (PINN) are applied to the reconstruction of the ship propeller wake field. First, the principle and basic framework of PINN were introduced. Then, the Burgers equation was selected to verify the feasibility of PINN in solving partial differential equations. After that, the propeller of KVLCC2 in open water is simulated using computational fluid dynamics (CFD) software STAR CCM+, and the flow field information of the KVLCC2 propeller is obtained. Based on the simulated flow field information data, the training sample set was constructed to train PINN. The trained PINN was used to infer the approximate solution of the governing equation at any time and space. Finally, the velocity and pressure distribution obtained by PINN were compared with the velocity and pressure distribution simulated by STAR CCM+. The results validate the reliability of PINN in propeller wake field reconstruction, which can be concluded that PINN can be applied to the reconstruction of the ship propeller wake field. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Intelligent Global Sensitivity Analysis Based on Deep Learning WU Shuchen, QI Zongfeng, LI Jianxun Journal of Shanghai Jiao Tong University    2022, 56 (7): 840-849.   DOI: 10.16183/j.cnki.jsjtu.2021.191 Abstract （1626）   HTML （56）    PDF（pc） （1418KB）（1359）       Knowledge map    Save This paper proposes an end-to-end method that combines deep learning and sensitivity analysis, which can perform gradient back propagation calculation sensitivity on the saved weight information while training the model. The structure and activation function of the depth model are specially designed to adapt to the subsequent sensitivity calculation. The experimental results conducted on a Boston house prices dataset, a track information fusion dataset, and the G function show that the proposed method is more accurate than classical methods such as Sobol’ method when the parameter distribution is uneven, and has a stronger robustness. Compared with the traditional neural network method, the accuracy of the proposed method is higher. The experiment proves that the sample parameter sensitivity obtained by the deep learning model can be used to optimize the model output. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(1)

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Fatigue Strength Analysis of Ship’s Welded Structures Based on Method of Notch Stress ZHEN Chunbo, LIU Shihao, ZHANG Aifeng, XING Shizhu, ZHANG Runze Journal of Shanghai Jiao Tong University    2025, 59 (4): 458-465.   DOI: 10.16183/j.cnki.jsjtu.2023.307 Abstract （1426）   HTML （7）    PDF（pc） （8884KB）（1359）       Knowledge map    Save To address the fatigue strength problem of ship structures, the notch stress method is applied to typical structural joints focusing on weld toe and weld root. First, the basic principles of notch stress method and the notch fatigue analysis process of ship structure are introduced. Then, six typical joint types are set up for the double bottom structure of a product oil tanker. The local notch stress analysis of the fatigue hot spot is conducted using the finite element sub-modeling technology, allowing the determination of the notch stress concentration factor at the weld toe and weld root. Finally, based on the harmonised common structural rules (HCSR), the fatigue load and load condition of the ship structure are calculated, and the fatigue analysis of six typical joint types is performed. The results show that under the same load conditions, the notch stress concentration factor at the weld toe of each hot spot is smaller than that at the weld root, and the joint type 3 has the lowest fatigue damage value among the joint types, which suggests this type can improve the fatigue resistance of the hull. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Interval Prediction Technology of Photovoltaic Power Based on Parameter Optimization of Extreme Learning Machine HE Zhizhuo, ZHANG Ying, ZHENG Gang, ZHENG Fang, HUANG Wandi, ZHANG Shenxi, CHENG Haozhong Journal of Shanghai Jiao Tong University    2024, 58 (3): 285-294.   DOI: 10.16183/j.cnki.jsjtu.2022.338 Abstract （646）   HTML （19）    PDF（pc） （1907KB）（1349）       Knowledge map    Save This paper proposes an interval prediction technology of photovoltaic (PV) power based on parameter optimization of extreme learning machine (ELM) model. First, the weighted Euclidean distance is proposed as the evaluation index of PV power prediction interval. The historical sample units are screened and the ELM training set is optimized. Then, a hybrid optimization algorithm for ELM parameters is proposed. The hidden layer input and output weights and biases parameters of the ELM model are optimized by using the elitist strategy genetic algorithm and quantile regression, and the trained model is used to predict the PV power range. Finally, an actual calculation example is constructed based on the historical data of PV power plants and weather stations. The PV power interval is predicted, and the results are compared with those obtained by other methods. The results of the calculation example show that the method proposed can greatly improve the accuracy of interval prediction while increasing the reliability of interval prediction. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Cited: CSCD(1)