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Multi-Targets Visiting Orbit Design Based on Numerical Method

Guidance,Navigation and control 2025年第1期05卷 139-148页

作者： Zenan Zhong Zhengxiang Sun Songyan Wang Tao Chao Ming Yang Control and Simulation Center Harbin Institute of Technology National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Beijing Institute of Space Long March Vehicle

In this paper, for the two/three targets in the circle orbit with the same altitude, a numerical method is presented to design an orbit for a spacecraft to visit multiple targets without maneuvering. First,for the two targets' case, an equation is established by the relationship of the two angles that both are the function of the time, one of the angles is the angle between two targets' geocentric position vectors at two times, and another one is the spacecraft flight angle during the two times. The visit time can be solved by the assumption that one of the visit times is known in the equation, and the orbit element can be transferred from the position and velocity vector determined by the solved visit time. Then, for the three targets' case, the problem is decoupled into two cases of two targets, and there is a public target in each case. Based on the method for two targets, two different orbits can be obtained by determining the visit time of the public target. The difference between two orbits can be expressed by the difference in velocity vectors of the public target at different times. A sampling method is utilized to select two velocity vectors with relatively small differences as initial values, the Newton iteration method is used to obtain visit times with higher accuracy. simulation results illustrate the effectiveness of the orbit design methods.

关键词： Orbit design method multiple targets visit circle orbit non-maneuvering

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Superhydrophobic ceramic membrane coupled with a biphasic solvent for efficient CO_(2)capture

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Green Energy & Environment 2025年第4期10卷 834-844页

作者： Kaili Xue Zhen Chen Xiaona Wu Heng Zhang Haiping Chen Junhua Li State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Beijing Key Laboratory of Pollutant Monitoring and Control in Thermoelectric Production Process School of EnergyPower and Mechanical EngineeringNorth China Electric Power UniversityBeijing102206China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing102206China

An innovative strategy was proposed by integration of membrane contactor(MC)with biphasic solvent for efficient CO_(2) capture from flue *** accessible fly ash-based ceramic membrane(CM)underwent hydrophobic modification through silane grafting,followed by fluoroalkylsilane decoration,to prepare the superhydrophobic membrane(CSCM).The CSCM significantly improved resistance to wetting by the biphasic solvent,consisting of amine(DETA)and sulfolane(TMS).Morphological characterizations and chemical analysis revealed the notable enhancements in pore structure and hydrophobic chemical groups for the modified *** of wetting/bubbling behavior based on static wetting theory referred the liquid entry pressure(LEP)of CSCM increased by 20 kPa compared to pristine *** with traditional amine solvents,the biphasic solvent presented the expected phase *** experiments demonstrated that the CO_(2) capture efficiency of the biphasic solvent increased by 7%,and the electrical energy required for desorption decreased by 32%.The 60-h continuous testing and supplemental characterization of used membrane confirmed the excellent adaptability and durability of the *** study provides a potential approach for accessing hydrophobic ceramic membranes and biphasic solvents for industrial CO_(2) capture.

关键词： Carbon capture Membrane contactor Hydrophobic modification Membrane wetting Biphasic solvent

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Optimal Deployment of Target Localization With Distance-Dependent Noise

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IEEE Internet of Things Journal 2025年

作者： Tian, Junxi Yang, Ming Chao, Tao Harbin Institute of Technology Control and Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150001 China

The cooperative localization of radio-based distance measurement is one of the methods to effectively suppress the nonline of sight (NLOS) ranging errors and as a complement to visual/global navigation satellite system (GNSS) localization for unmanned aerial vehicle (UAV) in GNSS challenging or denied environments due to its motility. Target localization can be regarded as a special case of cooperative localization. Meanwhile, the UAV can be considered as a ranging node when the ranging terminal is rigidly mounted on the UAV. To improve the target localization accuracy, the optimal deployment of anchor UAVs is one of the factors we consider when the noise characteristics are different. In this paper, the range-dependent noise in a real flight environment is considered and modeled separately according to the difference in noise characteristics. Subsequently, based on this correlation model, the Fisher information matrix (FIM) and a cost function derived from the A-optimal criterion are provided. Finally, the optimal deployment conditions and the minimum mean square error (MMSE) can be derived by minimizing the cost function as the objective. Through simulation and localization experiments, the validity of the optimal deployment conditions and the corresponding MMSE are verified. Meanwhile, it is concluded that when the noise is range-dependent, it will not affect the optimal deployment result but will significantly change the value of MMSE. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Achieving an Accurate Random Process Model for PV Power Using Cheap Data:Leveraging the SDE and Public Weather Reports

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CSEE Journal of Power and Energy systems 2025年第1期11卷 124-135页

作者： Yiwei Qiu Jin Lin Zhipeng Zhou Ningyi Dai Feng Liu Yonghua Song College of Electrical Engineering Sichuan UniversityChengdu610065China State Key Laboratory of the Control and Simulation of Power Systems and Generation Equipment Tsinghua UniversityBeijing 100087China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao999078China Department of Electrical Engineering Tsinghua UniversityBeijing100087China

Stochastic differential equation (SDE)-based random process models of renewable energy sources (RESs) jointly capture evolving probability distribution and temporal correlation in continuous time. It enabled recent studies to remarkably improve performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate daily SDE model for PV power be obtained that reflects its weather-dependent and non-Gaussian uncertainty in operation, especially when high-resolution numerical weather prediction (NWP) or sky imager is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed only using the data from low-resolution public weather reports. Specifically, for each day, an hourly parameterized Jacobi diffusion process recreates temporal patterns of PV volatility. Its parameters are mapped from the day's public weather reports to reflect varying weather conditions using a simple learning model. The SDE model jointly captures intraday and intrahour volatility. Statistical examination shows that the proposed approach outperforms a selection of the latest deep learning-based time series models on real-world data collected in Macao.

关键词： Photovoltaic(PV)power random process stochastic differential equation(SDE) stochastic optimization time series modeling uncertainty quantification weather report

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control, Modeling and Stability Analysis of DRU-based Grid-forming Converter

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Chinese Journal of Electrical Engineering 2025年第1期11卷 83-92页

作者： Wencong Wu Minzhi Wang Liang Tu Haiqing Cai Guanglei Yan Xiaohui Qu Haohan Gu Wei Chen State Key Laboratory of HVDC China Southern Power GridGuangzhou 510663China National Energy Power Grid Technology R&D Center Guangzhou 510663China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power Systems China Southern Power GridGuangzhou 510663China CSG Key Laboratory for Power System Simulation China Southern Power GridGuangzhou 510663China School of Electrical Engineering Southeast UniversityNanjing 210096China

Diode rectifier unit(DRU)-based high-voltage direct current(HVDC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof DRUs,a grid-forming(GFM)strategy forwind-turbine converters isnecessary to support offshore AC voltageand ***,the active power-synchronization control in traditional GFM converters is unsuitable for DRU-based GFM ***,the stability issue for DRU-based HVDC systems involving DRU-based GFM and grid-following(GFL)converters has not yet been *** solve these issues,this study begins with the characteristics of a DRU-based HVDC system and presents a control scheme for DRU-based GFM converters for power ***,the dq-frame impedance model of the DRU-based GFM converteris proposed for the stability analysis of the entire HVDC ***,a simulation platform is built to verify the model accuracy and system stability.

关键词： Diode rectifier unit offshore wind-powergeneration grid-forming converter impedance model stabilityanalysis

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Enhancing realism in LiDAR scene generation with CSPA-DFN and linear cross-attention via Diffusion Transformer model

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Neural Networks 2025年 189卷 107503页

作者： Ye, Shaoxun Di, Xiaoguang Liao, Ming Li, Ximing Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

Point cloud diffusion models have found extensive applications in autonomous driving and robotics. However, there is still a big gap between their generated LiDAR scene samples and real-world data in terms of visual quality. This discrepancy primarily arises from the loss of detailed information during the decoding process from latent space and the lack of guidance from global 3D structural information in the point cloud generation process, leading to distortions and artifacts in LiDAR scene samples. In this paper, we propose a novel LiDAR Diffusion Transformer Model that integrates Channel-Spatial Parallel Attention and Dilation Fusion Network (CSPA-DFN) with a linear cross-attention post-processing module to refine the generated LiDAR scene samples. Specifically, CSPA-DFN is designed to simultaneously emphasize detailed features across different channels and spatial locations in parallel, leveraging multi-scale dilated convolutions and channel grouping to preserve and enhance these detailed features. In order to provide global 3D structural information and balance performance and efficiency, we design a post-processing module that fuses voxelized features and range images using a linear ReLU cross-attention mechanism. Our approach is evaluated on the unconditional generation task using the KITTI-360 and nuScenes datasets, achieving the state-of-the-art results in LiDAR scene's generation quality. Furthermore, by incorporating semantic labels and camera views into the latent space, in addition to enhancing the model's semantic understanding capability for LiDAR scenes, our method also demonstrates additional performance improvements compared to previous works in terms of LiDAR scene's visual quality. The code implementation has been released on https://***/HITysx/LiDAR-Scene-Generation. © 2025 Elsevier Ltd

关键词： Photointerpretation

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Suppression of Phase Current Ripple and Torque Harmonics in Four-Module PMSM with Asymmetric Mutual Inductance

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IEEE Transactions on Power Electronics 2025年

作者： Liu, Kaiyuan Li, Chi Zheng, Zedong Li, Yongdong Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments Department of Electrical Engineering Beijing100084 China

In N-module motors, uniform carrier phase shifting (UCPS) by 2π/N between modules can effectively suppress torque harmonics caused by high-frequency switching of power devices. In multi-module motors with fractional-slot concentrated winding, the mutual inductance between adjacent windings is higher than that between non-adjacent windings. Although the conventional UCPS can reduce the torque sideband harmonics synthesized by the four modules, this asymmetric mutual inductance (AMI) paradoxically increases phase current ripple in each sub-module, leading to higher system losses. Aiming at this problem, this paper derives the mathematical model of N-module motor with AMI, analyzes the limitations of the traditional UCPS method that increases the phase current ripple. For different optimization objectives, two carrier phaseshifting angle optimized strategies are proposed to achieve integrated suppression of torque sideband harmonics and intramodule phase current ripples, with a genetic algorithm improving optimization speed. A threshold coefficient is introduced to achieve a tradeoff between phase current ripple and q-axis current harmonics, which can be adjusted to prioritize objectives based on user requirements. To enhance algorithm adaptability, the paper further investigates model variations under uneven torque distribution in sub-healthy states and proposes an improved comprehensive optimization algorithm, limiting torque sideband harmonics and current ripple in both healthy and sub-healthy conditions. Finally, the proposed algorithm is validated experimentally by a four-module permanent magnet synchronous motor. © 2025 IEEE.

关键词： Genetic algorithms

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Extraction and application of intrinsic predictable component in day-ahead power prediction for wind farm cluster

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Energy 2025年 328卷

作者： Yang, Mao Jiang, Renxian Yu, Xinnan Wang, Bo Su, Xin Ma, Chenglian The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing100912 China

With the continuous updating and iteration of artificial intelligence algorithms, prediction models emerge one after another, but the research and utilization of the predictability of wind power is still less. Therefore, this paper proposed a day-ahead power prediction method for Wind Farm Cluster (WFC) based on intrinsic predictable component extraction. Firstly, based on the difference of power distribution between wind farm and WFC, a method of Wind Power Curve (WPC) modeling for WFC is proposed, which provides the basis for establishing the Final Set of Wind Power Curve (FSWPC). Secondly, the Intrinsic Predictable Component (IPC) of wind power is extracted based on the FSWPC, and the Interference Component (IC) of wind power is separated to eliminate the influence of IC on IPC in the process of prediction. Thirdly, the historical similarity matching method with large threshold is used to predict the IC to make up for the numerical deficiency of the IPC. Finally, the proposed method was applied to a WFC in China to verify its effectiveness. Compared with the traditional prediction strategy, the NRMSE, NMAE and MAPE are reduced by 1.59 %, 1.15 % and 6.42 %, respectively. © 2025 Elsevier Ltd

关键词： Iterative methods

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AI-Optimized Genetic Algorithm for Enhancing Resource Recovery in the Circular Economy

AI-Optimized Genetic Algorithm for Enhancing Resource Recove...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Abderrahman Mansouri Bellat Abdelouahad Idriss Bennis Ali Siadat Fatiha Akef Modeling and Simulation of Intelligent Industrial Systems Laboratory (M2S2I) ENSET Mohammedia Hassan II University Casablanca Morocco Laboratory of Design Manufacturing and Control Arts et Metiers ParisTech Metz France Laboratory of Mechanical and Thermal Processes and Control (PCMT) ENSAM Rabat Mohammed V University in Rabat Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This paper presents a Genetic Algorithm (GA)- based optimization approach aimed at improving circular economy strategies by optimizing recyclability and economic value, while minimizing disassembly complexity and environmental impact. A multi-objective function is established, incorporating essential parameters such recyclability rate, component reuse value, disassembly expenses, carbon footprint, and waste minimization. The GA investigates an optimal balance between sustainability and economic viability using an evolutionary search method that identifies and enhances solutions throughout several generations. The dataset comprises 50 components of end-of-life products, categorized as metals (40%), plastics (30%), and electronic materials (30%), exhibiting recyclability rates between 35% and 95%. The results indicate that the GA attains a 25% enhancement in recyclability, a 30% reduction in disassembly complexity, and a 20% decrease in carbon footprint relative to traditional approaches. The suggested method offers a versatile and scalable decision-making instrument for manufacturers, recyclers, and policymakers, enhancing a more sustainable resource recovery system.

关键词： Systematics Instruments Computational modeling Search methods Decision making Complexity theory Sustainable development Optimization Carbon footprint Genetic algorithms

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A framework of day-ahead wind supply power forecasting by risk scenario perception

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IEEE Transactions on Sustainable Energy 2025年

作者： Yang, Mao Huang, Yutong Wang, Zhao Wang, Bo Su, Xin Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China China Electric Power Research Institute State Key Laboratory of Operation and Contr Ol of Renewable Energy & Storage Systems Beijing100912 China

Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method. © 2010-2012 IEEE.

关键词： Wind forecasting

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