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10th IEEE International power Electronics and Motion control Conference, IPEMC 2024 ECCE Asia

作者： Su, Bowen Sun, Kai Yang, Min Xiao, Yunhao Zhang, Kai Liu, Bo Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering China

ISBN: (纸本)9798350351330

Magnetic components play an important role in power electronic equipment, but there is still lack of accurate magnetic core loss estimation model. The data-driven methods are well-developed in recent years, and they are also more robust and accurate than conventional model-driven methods like Steinmetz Equation (SE) and improved Generalized Steinmetz Equation (iGSE). However, existing data-driven methods often focus on single excitation waveform like sinusoidal excitation waveform, which is not robust enough when facing complex excitation waveform with many high-frequency harmonic waveform. This paper presents a data-driven model based on attention mechanism which can capture the inner relationship within the input excitation waveform to present a more accurate core loss estimation results. And the comparison results with Multi-layer Perceptron (MLP) and Long Short-Term Memory(LSTM) models indicates that the proposed model has much more accuracy when facing complex excitation waveform. The proposed model can help to design magnetic components. © 2024 IEEE.

关键词： Facings

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Analysis of Subsynchronous Torsional Vibration of Wind-Thermal Bundling Delivery system via LCC-HVDC 6

Analysis of Subsynchronous Torsional Vibration of Wind-Therm...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Zhao, Yue Yan, Gangui Wang, Zhenyang Ren, Shuang Wang, Dazhong Guo, Jianyu Northeast Electric Power University Key Laboratory of Modern Power System Simulation Control and Green Power Technology Dept. Ministry of Education Jilin China

ISBN: (纸本)9798350346671

Recently, Wind-thermal bundled transmission via LCC-HVDC has become the main way of wind energy development and utilization in my country at present and in the future. The complex interaction between wind turbines, thermal power units, and LCC-HVDC needs to be further studied. In this paper, aiming at the direct-drive wind turbine group, thermal power unit and HVDC, the linearization model of the system is established, and the interactive coupling relationship between the connected equipment and the electrical damping of the thermal power unit is analyzed, and the mechanism of torsional vibration of thermal power unit shafting caused by wind-thermal bundling via LCC-HVDC is studied. mechanism. Based on the complex torque coefficient method, the influence of factors such as wind speed, control parameters, HVDC transmission power and control parameters on the electrical damping coefficient of thermal power units is studied. Finally, a time-domain simulation model is built in PSCAD/EMTDC, and the effectiveness of the electrical damping coefficient analysis results is verified through time-domain simulation and time-frequency analysis. © 2023 IEEE.

关键词： Wind

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A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

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Computers, Materials & Continua 2024年第9期80卷 4497-4512页

作者： Tianqing Yuan Na Li Hao Sun Sen Tan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Department of Electrical Engineering Northeast Electric Power UniversityJilin132012China College of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China Center for Research on Microgrids Department of EnergyAalborg UniversityAalborg9220Denmark

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting ***,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data ***,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on ***,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available *** show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

关键词： Lithium-ion batteries state of charge state of health cyber-attacks genetic algorithm back propagation neural network

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Optimal Scheduling for Agricultural Multi-Energy Park with Combined Wind-Solar-Biomass Energy Supply

Optimal Scheduling for Agricultural Multi-Energy Park with C...

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2023 International Conference on power system Technology, powerCon 2023

作者： Wang, Xuan Zhao, Qianyu Wang, Shouxiang Tianjin University Key Laboratory of Smart Grid of Ministry of Education Tianjin Key Laboratory of Power System Simulation and Control Tianjin China

ISBN: (纸本)9798350300222

Agricultural multi-energy park (AMEP) is an essential pathway for upgrading and transforming traditional agriculture, and the utilization of renewable energy can contribute to its sustainable development. In this context, this paper proposes an optimal scheduling method for AMEP with combined wind-solar-biomass energy supply. The biomass energy contained in waste from the user side can be reused and combined with wind and solar energy to achieve a diversified supply of renewable energy. Besides, a biogas heat-electricity cogeneration with adjustable thermoelectric ratio is introduced, enhancing the flexibility of electricity and heating supply for AMEP. In addition, an adder carbon tax mechanism is considered to incentivize energy conservation and emissions reduction of AMEP. simulation results demonstrate that the proposed optimal scheduling model can more effectively utilize renewable energy, leading to significant advantages in enhancing the flexibility, economy, and low carbon of AMEP. © 2023 IEEE.

关键词： Biomass

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Optimal Scheduling for Multi-energy system Considering Coupling of Molten Medium Hydrogen Production and Pipeline Energy Storage 3

Optimal Scheduling for Multi-energy System Considering Coupl...

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3rd New Energy and Energy Storage system control Summit Forum, NEESSC 2023

作者： Wang, Xuan Zhao, Qianyu Wang, Shouxiang Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin Key Laboratory of Power System Simulation and Control Tianjin China

ISBN: (纸本)9798350309348

Existing hydrogen production technologies always have weakness of large carbon emission, and there are still many research gaps in coupling with multi-energy flow. To this end, this paper proposes an optimal scheduling model for multi-energy system (MES) considering coupling of molten medium hydrogen production (mmH2P) and pipeline energy storage (PES). The mmH2P is introduced into MES to achieve the in-situ green conversion of gas and the zero-carbon-emission hydrogen production while obtaining solid carbon. Further, a Refined PtGmmH2P mode is proposed combining mmH2P, power-to-gas and hydrogen storage. Besides, the PES due to different transmission inertia of energy flows is considered, and its coupling coordination promotion with Refined PtG-mmH2P is further analyzed. Case study proves that the proposed scheduling model of MES can achieve the win-win of economy, low carbon and high renewable energy consumption rate. ©2023 IEEE.

关键词： Pipelines

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Dynamic Modeling of key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

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Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

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Optimization Strategy for Virtual Energy Storage of Electric Vehicles to Participate in Multiple Ancillary Services 5

Optimization Strategy for Virtual Energy Storage of Electric...

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5th International Conference on Clean Energy and Electric power Engineering, ICCEPE 2024

作者： Guo, Qi Gu, Haohan Zhao, Yifei Tu, Liang Chen, Qifang Cai, Haiqing Chen, Wei Chen, Zhihao Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou510663 China School of Electrical Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798350390315

When EVs participate in multiple ancillary services of the power grid, due to the lack of a unified management scheduling plan, various ancillary service demands compete for limited energy storage resources. This would lead to conflicts when the virtual energy storage of EV participates in the regulation of multiple ancillary services. In order to solve the above problems, a reasonable and feasible optimization strategy for virtual energy storage of EVs is formulated to alleviate the conflict problems of ancillary services. The validity of the proposed method is verified by simulation cases. © 2024 IEEE.

关键词： control strategies electric vehicles multiple auxiliary services

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Optimal Scheduling of Pumped Storage power Station with Double-Fed Variable Speed Unit Based on Reservoir Regional Operation Strategy 4

Optimal Scheduling of Pumped Storage Power Station with Doub...

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4th Asia-Pacific Conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Ma, Yuhan An, Yining Ge, Xiaohui Zhao, Bo State Grid Zhejiang Electric Power Co. Ltd. Research Institute Zhejiang Hangzhou China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Jilin Jilin China

ISBN: (纸本)9798350359985

The conventional pumping unit lacks the ability to continuously adjust input power during pumping operations, and exhibits poor adaptability when integrating new energy sources into the grid. In this study, we propose a novel 'domain operation' strategy that allocates resources based on real-time reservoir conditions across all levels of hydropower stations, while optimizing operations through the implementation of doubly fed units. © 2024 IEEE.

关键词： Hydroelectric power

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Diagnosis of unbalanced bearing fault samples based on cycle generative adversarial networks

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Neural Computing and Applications 2025年 1-21页

作者： Huang, Rihao Ma, Liangyu Hu, Jingchen Duan, Xiaochong Gao, Haitian Ma, Jin School of Control and Computering Engineering North China Electric Power University Baoding071003 China Baoding Key Laboratory of State Detection and Optimization for Integrated Energy System Baoding071003 China

The robust feature extraction capability of deep learning (DL) has rendered it a popular choice for fault diagnosis in various industries. However, the accuracy of fault diagnosis models is often compromised due to an imbalance between normal and faulty data during the production process. To address this issue, this study proposes an unbalanced bearing fault sample diagnostic method based on a cycle generative adversarial network, using vibration fault diagnosis of rotating machinery rolling bearings as an illustrative example for validation purposes. The method utilizes continuous wavelet transform (CWT) to convert the original vibration signal into time–frequency maps, which are then utilized as input for the cycle generative adversarial network. To address issues related to unstable training and timely convergence of the model, spectral normalization and weight decay techniques are introduced, thereby enhancing the performance of the improved cycle generative adversarial network in generating additional fault samples. Finally, the Swin Transformer model is employed for fault diagnosis and compared with other methods such as Random Forest (RF), Sparse Autoencoder (SAE), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The diagnostic experiment encompasses sample expansion, imbalanced proportion between normal and faulty samples, as well as scenarios involving limited sample sizes. Experimental results demonstrate that the proposed method can generate higher quality synthetic samples when training data is scarce;moreover, utilizing the Swin Transformer for diagnosis yields superior accuracy compared to alternative approaches such as RF, SAE, SVM, or CNN models—thus showcasing significant potential in addressing imbalanced data within fault diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

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Journal of Modern power systems and Clean Energy 2022年第6期10卷 1738-1749页

作者： Lin Cheng Yuxiang Wan Yanglin Zhou David Wenzhong Gao State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Electrical and Computer Engineering University of DenverDenverUSA IEEE

Battery energy storage(BES)systems can effectively meet the diversified needs of power system dispatching and assist in renewable energy *** reliability of energy storage is essential to ensure the operational safety of the power ***,BES systems are composed of battery *** suggests that BES performance depends not only on the configuration but also on the operating state over different lifetime *** lack of safety and reliability is the main bottleneck preventing widespread applications of BES ***,a reliability assessment algorithm and a weak-link analytical method for BES systems are proposed while considering battery lifetime ***,a novel lithium-ion battery model is proposed to identify the degradation rate of solid electrolyte interphase film formation and capacity *** impacts of different operating conditions are considered in stress factor ***,a reliability assessment algorithm for a BES system is introduced based on a universal generating *** innovative weak-link analytical method based on the reliability importance index is proposed that combines the evaluation results of state-oriented and state-change-oriented indexes through an entropy weight *** model,algorithm,indexes,and the usefulness are demonstrated in case studies based on aging test data and actual bus operating *** results demonstrate the effects of the battery status and working conditions on BES ***-link analysis is also used to assist BES systems in avoiding short-board batteries to achieve long lifetimes and efficient operation.

关键词： Battery energy storage lifetime degradation operational reliability capacity plummeting weak-link analysis

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