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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Zeng, Guanming Tu, Liang Zhu, Yihua Li, Chengxiang Liu, Mingqun He, Peng State Key Laboratory of HVDC and CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&D Centre Guangdong Provincial Key Laboratory of Intelligent Operation Control for New Energy Power System Guangdong Guangzhou China Yunnan Power Grid Co. Ltd. Electric Power Research Institute Yunnan Kunming China

ISBN: (纸本)9798350322699

With the construction of new power system, high-capacity renewable energy sources including wind power are integrated into the power system, whose transient response characteristics have a significant impact on the safe and stable operation of both renewable energy sources and power system. Real-time simulation has become an effective method to study interaction between high-capacity renewable energy sources and power system because of its high accuracy and reliability, and precise wind turbine real-time simulation model is of great significance in research mentioned above. Therefore, the wind turbine control hardware in loop (CHIL) real-time simulation method is proposed in the paper, whose feasibility and accuracy are also verified by comparing simulation data from CHIL platform and on-site testing data. It's further used in the analysis of large-scale offshore wind farm cluster and connected power system as a practical reference case. © 2023 IEEE.

关键词： control hardware in loop power system analysis real-time simulation wind turbine

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Clustering and Equivalent Modeling Method for Direct-Drive Wind Farms Based on Particle Swarm Neural Network 4

Clustering and Equivalent Modeling Method for Direct-Drive W...

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4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Tu, Liang Huang, Libin Guo, Haiping Hu, Binjiang Zhang, Jingyue Luo, Hui State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&d Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China

ISBN: (纸本)9798331518066

In the realm of large-scale integration of wind farms or clusters into power grids, the fault transient characteristics of these grids have experienced significant transformations. Unfortunately, the prevalent single-machine equivalent models are inadequate in accurately depicting the fault transient behavior of wind farms or clusters. Moreover, their simulation computations are exceedingly intensive, resulting in suboptimal simulation efficiency. To address these challenges, this paper introduces a clustering and equivalent modeling approach tailored for direct-drive wind farms, leveraging the capabilities of a particle swarm neural network. Initially, a transient model for direct-drive wind turbines is formulated, and fault characteristic influence factors are established through theoretical analysis. These factors encompass the distance between the wind turbine and the point of common coupling (PCC), the implementation of DC-side current limiting measures, wind speed, and reactive power at the outlet. Following this, the Euclidean distance of the influence factor set is computed, and the initial cluster centers of wind turbines are derived using an enhanced max-min distance method. Ultimately, an equivalent clustering model is formulated by utilizing the influence factors and initial cluster centers as the training set. Simulation outcomes reveal that, in comparison to the single-machine equivalent model, this method enhances simulation efficiency by a range of 62% to 67% and reduces the steady-state error in fault current simulation by 0.8% to 1.9%. © 2024 IEEE.

关键词： Windmill

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Aeronautical Communication Traffic Simulation Based on Kernel Density Estimation Method 12

Aeronautical Communication Traffic Simulation Based on Kerne...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Zhang, Yingxi Jiang, Dapeng Shao, Fengwei Wu, Tong Liang, Xianling Chen, Jingfeng China Ship Development and Design Center Wuhan China Shanghai Spacecom Satellite Technology Ltd. Shanghai China Shanghai Jiao Tong University Shanghai China State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis China

ISBN: (纸本)9798350351019

Low Earth Orbit (LEO) satellite networks have been emerging as a revolutionary solution for ubiquitous communication in the upcoming sixth generation (6 G) wireless communication network. Efficient resource allocation in these satellite networks requires precise analysis of communication traffic demand. To simulate an accurate representation of the spatial-temporal communication traffic pattern worldwide, this paper utilizes Kernel Density Estimation (KDE) to simulate aircraft positions and employs a time-varying communication intensity model. The effectiveness of this approach is validated through numerical simulation results. © 2024 IEEE.

关键词： Satellite communication systems

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Research on ASVs Formation Obstacle Avoidance Based on Virtual Structure and Artificial Potential Field

Research on ASVs Formation Obstacle Avoidance Based on Virtu...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Yihai Zhang, Jinfen Han, Bing Ma, Feng Tian, Wuliu State Key Laboratory of Maritime Technology and Safety Shanghai Wuhan China Intelligent Transportation System Research Center Wuhan University of Technology Wuhan430070 China Shanghai Ship and Shipping Research Institute Co. Ltd. Shanghai200137 China Beibu Gulf University Qinzhou535011 China

ISBN: (纸本)9789819639649

The new mode of ship formation navigation can significantly enhance the level of ship intelligence and improve navigation efficiency. To address the obstacle avoidance problem of ship formations under obstacle-constrained environments, a control model for Autonomous Surface Vessel (ASV) formation is constructed using the virtual structure method, decomposing the formation control problem into path tracking control and path parameter coordination. Considering both dynamic/static obstacles during the navigation of ASVs formation, an obstacle avoidance control strategy based on artificial potential fields is proposed. It achieves formation maintenance and obstacle avoidance control for ASVs in complex environments. Simulation results show that ASVs can track paths in desired formation and effectively avoid obstacles. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Ships

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Degree and Classification of Winding Deformation Faults Based on Least-Squares Fitting of Lissajous Trajectory Coefficient Recognition 3rd

Degree and Classification of Winding Deformation Faults Base...

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3rd International Conference on Information Control, Electrical Engineering, and Rail Transit, ICEERT 2023

作者： Chen, Jianquan Zhaung, Yi Chen, Ruichuang Ma, Hongde Wu, Zhaoxiao Yunfu Power Supply Bureau of Guangdong Power Grid Co. Guangdong Yunfu527300 China Guangzhou Huali Science and Technology Vocational College Guangdong Guangzhou510000 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400000 China

ISBN: (纸本)9789819746194

The power transformer is the most commonly used equipment in the power system, and winding deformation is one of the most frequent transformer faults. The short-circuit impedance method based on ΔU–I to construct the Lissajous figures is a newly proposed winding deformation detection method in recent years, but there are still problems such as difficulty in detecting the small deformation of the winding, and it is difficult to distinguish between different types of faults. In this paper, on the basis of the existing ΔU–I constructing Lissajous figures, a winding deformation detection method based on the least-squares fitting Lissajous track coefficient identification is proposed, which can effectively detect the winding deformation and determine the degree and type of faults. Firstly, the primary side current and voltage difference are used to construct the Lissajous figures, and the lower half of the Lissajous trajectory is excised to change the trajectory into a single-valued function;then the least-squares method is used for fitting, and the identification of winding deformation is carried out based on the coefficients of the power series of the fitted coefficients. The research results show that the method can effectively identify the small deformation of the winding and its degree of deformation, and distinguish the amplitude deformation and axial deformation, which has a certain engineering value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Least squares approximations

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Research Progress of the Dissolved Gas Analysis in Synthetic Ester Insulating Oil under Electrical Faults

Research Progress of the Dissolved Gas Analysis in Synthetic...

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2022 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Ouyang, Liangxuan Wang, Feipeng Chen, Xingyu Song, Chao State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China Jiangsu Shuangjiang Energy Technology Co Jiangsu China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

Transformers will be subjected to electrical, thermal, and mechanical stresses during operation, causing decomposition of the insulation materials and consequently generating gas in the oil. Early transformer faults including electrical faults can be detected in time through DGA (Dissolved Gas-in-oil Analysis) which can reflect the state information of insulating oil transformers. Recently, the application of synthetic insulating oil in large power transformers could increase due to excellent fire resistance, environmental protection, strong oxidation stability, etc. Due to the differences in molecular structure between synthetic ester insulating oils and mineral insulating oils as well as natural ester insulating oils, there are differences in the characteristics of dissolved gas in the oil produced under electrical faults, resulting in different limits of dissolved gas in the oil under normal and fault conditions. In order to ensure the safe and reliable operation of synthetic ester insulating transformers, this paper reviews the analysis on faulty gas production of synthetic ester insulating oil and summaries the commonly used diagnostic methods for synthetic insulating oil under electrical faults. © 2022 IEEE.

关键词： Esters

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A Look-Up Table Based Inverter Nonlinearity Compensation Method Considering Parameter Inconsistency of Upper and Lower Bridge Arms

A Look-Up Table Based Inverter Nonlinearity Compensation Met...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yonghe Huang Dianguo Xu Yan Xu Yong Yu Junyu Zhao Xubo Gong School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China The State key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

In motor drive systems, voltage source inverter (VSI) nonlinearity will produce voltage error, cause current harmonics, and affect the control performance. Among the VSI nonlinearity factors, the parasitic capacitance is the fundamental cause of the nonlinear relationship between voltage error and phase current. If the parameter inconsistency of the upper and lower bridge arm is not considered, the voltage error is an odd function. However, in practical applications, the parameters of the upper and lower bridge arms are usually slightly different, and the voltage error will no longer be an odd function. Considering the influence of parameter inconsistency, this article proposes an accurate look-up table (LUT) based method for VSI nonlinearity compensation. By injecting positive staircase current and negative staircase current separately in self-commissioning process, an accurate voltage error LUT is obtained through numerical fitting methods. The proposed method does not require any parameter of power devices. Experimental results verify the effectiveness of the proposed method.

关键词： Industrial electronics Motor drives Accuracy Voltage source inverters Fitting Bridge circuits Table lookup

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Theoretical Modeling and Performance Analysis on the Linear Electromagnetic Actuator with High Nonlinear Dynamic Negative Stiffness

SSRN

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SSRN 2024年

作者： Wang, Chuchao Lu, Shizhou Liu, Xiaohan Mo, Wenyin Zhang, Bin Li, Kai Sun, Lining Mechanical Electrical & Information Engineering Shandong University Weihai264209 China School of Mechanical Engineering Dalian University of Technology Dalian116081 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

Electromagnetic mechanisms are introduced to adjust the stiffness and improve the performance of mechanical systems, which have been widely researched. In this paper, the transient performance of a novel linear solenoid elastic actuator with higher nonlinear negative stiffness and speed by introducing the negative stiffness mechanism is analyzed and predicted. The actuator can achieve an initial theoretical stiffness of -20.96 N/m at 1 A current and a maximum stroke of 9 mm in 0.5 s. The performance indicators including peak time, overshoot, and steady-state displacement are obtained through electromagnetic-mechanical coupled modeling. The influence of system parameters including natural frequency, equivalent stiffness and damping coefficient on performance indicators is also studied. First, the approximate analytical model of the electromagnetic force is derived by constructing an innovative magnetic field distribution model. Second, the nonlinear vibration equation is solved theoretically to obtain the analytical results of the performance indicators and system parameters. Finally, the transient displacements and performance indicators obtained from the analytical model, the numerical model established in Simulink software and the simulation model obtained in Adams software are compared. The maximum errors of the peak time, overshoot and stable displacement through the analytical model and experiment are 2.56 ms, 9.37% and 0.32 mm, respectively. The theoretical and experimental results show the nonlinear effects of input currents on performance indicators and system parameters. The overshoot has a strong positive following feature on the damping coefficient, but the vibration frequency mainly depends on the equivalent stiffness of the system. This research provides a reference for the transient solution of nonlinear electromagnetic-dynamic coupling models and performance improvement of linear electromagnetic actuators. © 2024, The Authors. All rights reserved.

关键词： Stiffness

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Prediction and Analysis of Large-Scale Stock Market Risk Correlation Structure

Prediction and Analysis of Large-Scale Stock Market Risk Cor...

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High Performance and Smart Computing (HPSC), IEEE International Conference on

作者： Wang Ting Pan Zhijie Zheng Yanting Key Laboratory of System Software and State Key Laboratory of Computer Science Institute of Software CAS Beijing China School of Economy Beijing Technology and Business University Beijing China

ISBN: (数字)9798331596637

ISBN: (纸本)9798331596644

The prediction of the high-dimensional dependence structure among global stock markets is an advanced topic in financial risk management. However, the complex structure characterized by high dimensionality, nonlinearity, and asymmetry, presents significant challenges for conventional financial models (e.g. Autoregressive Moving Average Model) to systematically and efficiently predict and analyze large-scale cross-market correlation structures. The original program for preprocessing the correlation structure decomposition across 36 countries was optimized, enabling it to scale up to hundreds of threads in parallel. It achieved an acceleration of over 25.88 times on 30 threads compared to the original program. The advancement of deep learning technology has provided a solution to this issue. By constructing models such as Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Gated Recurrent Unit Networks (GRU), along with Average Weighted Ensemble Algorithm(AWEA), Encoder-Decoder Algorithm with Attention(EDAA), and Random Forest Ensemble Algorithm(RFEA), we can achieve financial time series forecasting based on a Patched Bivariate Fréchet Copula model for dependence structures. This provides a rapid decision-making basis for preventing significant financial risks. The experimental results show that the RFEA model outperforms the other two ensemble neural network algorithms in predicting correlation structures. And also the RFEA model showed lower error (e.g., the MSE of global alpha is about 89% of the other two algorithms) and better fit (global alpha and gamma fit to 99.5% and 98.7%, respectively). It demonstrates good predictive performance and high operational efficiency, with certain application value for analyzing the correlation structure of the stock market.

关键词： Deep learning Correlation Accuracy Instruction sets Biological system modeling Predictive models Prediction algorithms Stock markets Long short term memory Autoregressive processes

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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