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IEEE Student Conference on Electric Machines and systems

作者： Jiawen Cao Dongbao Lv Xindi Liu Changgang Li Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

With the widespread integration of renewable energy sources (REs), the number of power system scenarios to be analyzed has increased significantly, and the system structure has become increasingly complex, which increases the computational burden of power system dynamic simulations and leads to substantial computation time. Therefore, there is an urgent need to improve the efficiency of dynamic simulation to meet the requirements of advanced applications. The uEITSA method adopts a fixed system time-step in the overall simulation, which makes it challenging to balance simulation accuracy and efficiency. Therefore, a system time-step adjustment strategy is proposed in this paper to optimize the uEITSA method, implemented in the open-source Simulation Toolkit for Electrical Power systems (STEPS). Firstly, the system time-step adjustment strategy based on elementary transfer function blocks (ETFBs) is introduced. Secondly, the implementation of the proposed strategy in STEPS software is described in detail. Finally, the simplified Shandong Power Grid model is tested to demonstrate that the proposed system time-step adjustment strategy can improve simulation efficiency without jeopardizing simulation accuracy.

关键词： Renewable energy sources Transient response Accuracy Computational modeling Power system dynamics Transfer functions Software Power grids Robustness systems simulation

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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第42届中国控制会议

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network(TL-DNN) was proposed in this paper. Its specific implementation steps include(1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models(random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Research on Microgrid Energy Storage Scheduling Strategy Incorporating Electric Vehicles Based on the DQN Algorithm

Research on Microgrid Energy Storage Scheduling Strategy Inc...

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Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Peiqi Xin Hongtao Wang Haobo Wang Zhen Song Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

This study focuses on the scheduling of a microgrid integrated with electric vehicles, employing a reinforcement learning algorithm to devise an optimal economic operation strategy. The approach addresses the challenges of renewable energy generation’s randomness and the economic and safety concerns arising from the extensive integration of electric vehicles into the microgrid. The paper explores the reinforcement learning-based energy management of storage devices within the microgrid. A Deep Q-Network (DQN) method is utilized to formulate the microgrid storage scheduling problem as a Markov Decision Process (MDP). The study involves a detailed simulation of a residential user microgrid, considering various parameters such as battery storage capacity, charging and discharging rates, and electric vehicle load models. The reinforcement learning algorithm demonstrates the capability of real-time response to power changes in the microgrid, ensuring operational cost minimization even under multiple input uncertainties.

关键词： Renewable energy sources Uncertainty Supply and demand Microgrids Reinforcement learning Electric vehicles Scheduling

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Small-size temperature/high-pressure integrated sensor via flip-chip method

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Microsystems & Nanoengineering 2024年第4期10卷 153-162页

作者： Mimi Huang Xiaoyu Wu Libo Zhao Xiangguang Han Yong Xia Yi Gao Zeyu Cui Cheng Zhang Xiaokai Yang Zhixia Qiao Zhikang Li Feng Han Ping Yang Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesXi’an Jiaotong UniversityXi’an 710049China Xi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and System Xi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China School of Mechanical Engineering Xi’an Jiaotong University710049 Xi’anChina School of Instrument Science and Technology Xi’an Jiaotong University710049 Xi’anChina Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Sensing Chongqing Academician WorkstationChongqing 2011 Collaborative Innovation Center of Micro/Nano Sensing and Intelligent Ecological Internet of ThingsChongqing Technology and Business UniversityNan’an DistrictChongqing 400067China Xi’an Aerospace Yuanzheng Fluid Control Co.Ltd. Xi’an 710049China

Hydraulic technology with smaller sizes and higher reliability trends,including fault prediction and intelligent control,requires high-performance temperature and pressure-integrated *** designs rely on planar wafer-or chip-level integration,which is limited by pressure range,chip size,and low *** propose a small-size temperature/high-pressure integrated sensor via the flip-chip *** pressure and temperature units are arranged vertically,and the sensing signals of the two units are integrated into one plane through silicon vias and gold–gold bonding,reducing the lateral size and improving the efficiency of signal *** flip-chip technique ensures a reliable electrical connection.A square diaphragm with rounded corners is designed and optimised with simulation to sense high pressure based on the piezoresistive *** temperature sensing unit with a thin-film platinum resistor measures temperature and provides back-end high-precision compensation,which will improve the precision of the pressure *** integrated chip is fabricated by MEMS technology and packaged to fabricate the extremely small integrated *** integrated sensor is characterised,and the pressure sensor exhibits a sensitivity and sensitivity drift of 7.97mV/MPa and−0.19%FS in the range of 0–20 MPa and−40 to 120℃.The linearity,hysteresis,repeatability,accuracy,basic error,and zero-time drift are 0.16%FS,0.04%FS,0.06%FS,0.18%FS,±0.23%FS and 0.04%FS,*** measurement error of the temperature sensor and temperature coefficient of resistance is less than±1°C and 3142.997 ppm/℃,*** integrated sensor has broad applicability in fault diagnosis and safety monitoring of high-end equipment such as automobile detection,industrial equipment,and oil drilling platforms.

关键词： size corners sizes

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Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

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Chinese Journal of Aeronautics 2022年第10期35卷 301-312页

作者： Jiqiang ZHANG Xiangwei KONG Xueyi LI Zhiyong HU Liu CHENG Mingzhu YU School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of EducationNortheastern UniversityShenyang 110819China Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment Northeastern UniversityShenyang 110819China Angang Steel Company Limited Anshan 114021China

Bearing pitting,one of the common faults in mechanical systems,is a research hotspot in both academia and *** fault diagnosis methods for bearings are based on manual experience with low diagnostic *** study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout *** separable convolution extracts features from the raw bearing vibration signals,during which a 3×1 convolutional kernel with a one-step size selects effective features by adjusting its *** similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller *** spatial dropout regularization method focuses on bearing signal fault features,improving the independence between the bearing signal features and enhancing the robustness of the model.A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability *** validate the effectiveness of the proposed method,we collect raw vibration signals from bearings in eight different health *** experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.

关键词： Batch normalization Convolutional neural network Fault diagnosis Similarity pruning Spatial dropout

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Frequency Domain Stability Analysis and Simulation of New Energy Power Generation systems

Frequency Domain Stability Analysis and Simulation of New En...

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Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Haobo Wang Hongtao Wang Peiqi Xin Zhen Song Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

Stability analysis of grid-connected inverters is advanced through impedance analysis in this study, which leads to the development of a comprehensive mathematical model and simulation. control parameters, notably the voltage inner-loop control parameter $K_{v}$, the proportional gain $K_{p}$ of the phase-locked loop (PLL), and the integral gain $K_{i}$, were meticulously adjusted to assess their effects on subsynchronous oscillations. It was determined that while the proportional gain $K_{p}$ and integral gain $K_{i}$ have a marginal impact on sub-synchronous oscillations, the voltage inner-loop control parameter $K_{i}$ significantly influences these oscillations. The insights gained from this analysis contribute to a deeper understanding of, and potential solutions for, synchronous oscillation issues. The ability to mitigate such oscillations facilitates the integration of a higher proportion of renewable energy sources, thereby enhancing the stability and reliability of the electrical power system. This enhancement is of critical importance in the pursuit of sustainable energy transitions and the advancement of clean energy sources.

关键词： Power system stability Stability analysis Mathematical models Inverters Power system reliability Reliability Voltage control

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Virtual Oscillator Grid-Forming Inverter Stability Analysis Based on Impedance Model

Virtual Oscillator Grid-Forming Inverter Stability Analysis ...

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China International Electrical and Energy Conference (CIEEC)

作者： Shuiyuan Guo Hua Ye Jialun Yang Xiaodong Chu Qianying Mou Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Grid-forming (GFM) inverters are DC-AC converters that regulate their AC terminal frequency and voltage in response to real-time measurements. Virtual oscillator control (VOC) is an emerging strategy of grid-forming controllers. Unlike conventional phasor-based control methods such as droop control and virtual synchronous machine control, VOC is a time-domain controller that global convergence can be almost guaranteed by adding a nonlinear element. In this article, the state space model of a VOC-based inverter system connected to an infinite bus is established to access the steady-state values. In order to study the effect of nonlinear terms on the system stability, a detailed impedance model of the VOC controller is proposed. Numerical simulations validate the impedance model. Through the impedance-based method, how the nonlinear term can influence system stability is analyzed.

关键词： Analytical models Voltage measurement Stability criteria Power system stability Numerical models Impedance Voltage control

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Prediction of Hybrid Asynchronous Frequency Hopping Pattern based on Deep Learning in Wideband Spectrum

Prediction of Hybrid Asynchronous Frequency Hopping Pattern ...

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2023 China Automation Congress, CAC 2023

作者： Wang, Ya Wang, Jiaqi Yuan, Shuai Liu, Naijin Lu, Yuanming Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Unit 63893 of Pla Luoyang China

ISBN: (纸本)9798350303759

In non-cooperative scenarios, wideband frequency hopping (FH) communication reconnaissance including FH signal detection, parameter estimation, and network sorting under single-channel reception is challenging. The FH pattern contains the most information about FH signals. So it is the core of FH parameter estimation. Based on the task analysis, this paper proposes a blind prediction framework combining deep learning (DL) and clustering methods to improve the accuracy and efficiency of FH pattern estimation for mixed signals in the wideband spectrum. The unique advantage of this framework is that it requires no prior signal information and anchors but exploits the inherent time-frequency (TF) properties of asynchronous FH signals. It has a strong generalization ability and can adapt to signals of any shape. Moreover, a new comprehensive evaluation metric, normalized root mean square error including missed detection (NRMSE-MD), suitable for sequence data prediction is proposed to evaluate the level of missed detection and false detection in signal monitoring. Experimental results demonstrate the superiority of the proposed framework and the effectiveness of the proposed evaluation metric. © 2023 IEEE.

关键词： Forecasting

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Technical Research on Simulation and Predictive Analysis of Wind Farms in Power systems 4

Technical Research on Simulation and Predictive Analysis of ...

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

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

ISBN: (纸本)9798331518066

To address the issue of insufficient accuracy in wind power output forecasting, this paper proposes a Support Vector Machine (VSM) model based on similar day matching and kernel entropy component analysis for wind power prediction. The model effectively extracts similar day features through the similar day approach and optimizes feature representation by combining it with kernel entropy component analysis (KECA), ultimately employing the SVM model for wind farm power forecasting. Simulation results demonstrate that using this model for optimizing wind power generation predictions enhances the accuracy of wind power forecasts, surpassing the precision of single-method predictions. The Similar Day-KECA-SVM method leverages historical data from similar days and features extracted through KECA, with SVM performing the final prediction. This approach adeptly handles complex patterns and nonlinear relationships in wind power forecasting, thereby achieving superior predictive performance. © 2024 IEEE.

关键词： Wind forecasting

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Sub-Synchronous Oscillation Suppression in Series-Compensated Grid-Connected Doubly-Fed Wind Turbines Utilizing Virtual Inductance on the Rotor Side

Sub-Synchronous Oscillation Suppression in Series-Compensate...

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China International Electrical and Energy Conference (CIEEC)

作者： Haobo Wang Hongtao Wang Peiqi Xin Zhen Song Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

To tackle the issue of Sub-Synchronous control Interaction (SSCI) in doubly fed wind turbines featuring series capacitor compensation, a novel approach is undertaken by creating an impedance model for the Doubly Fed Induction Generator (DFIG). This model utilizes a power outer-loop control method for decoupling, in alignment with IEEE standards. The study extensively examines the genesis and underlying causes of sub-synchronous oscillations. A novel rotor-side suppression strategy, featuring the integration of virtual inductance into the DFIG's rotor control loop, is proposed to mitigate oscillations induced by series-compensated transmission line parameters. Comparative analysis indicates that the inclusion of virtual inductance effectively quells sub-synchronous oscillations. Additionally, a Simulink-based grid-connected DFIG model is constructed, and time-domain simulations are conducted to validate the model's accuracy in predicting grid-connected sub-synchronous oscillations and confirm the efficacy of the proposed suppression technique.

关键词： Inductance Power transmission lines Doubly fed induction generators Rotors Predictive models IEEE Standards Wind turbines

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