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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zhang, Jiamei Sun, Kai Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

ISBN: (纸本)9781665464543

The voltage support capability of renewable resources (RES) is emphasized by grid codes for stable and secure operation faced with grid faults. However, there is another problem occurs when the penetration of RES is boosting. The capability occupation due to injected power for voltage support leads to the decrease of output power for loads, which may result in frequency issues when the inertia of the system decreases. To guarantee the voltage and frequency security during fault ride through (FRT) process, this paper proposes an optimal scheduling scheme for PV-BES system considering voltage support and frequency regulation. In the optimal model, in order to map the voltage and frequency constraints into power constraints, the evaluation model of required injected power for voltage support is formulated firstly. Based on center of inertia theorem and time scale characteristics of FRT, the frequency deviation is represented by distributed generator (DG) adjustment. The voltage and frequency constraint are incorporated into the optimal scheduling model and the quasi-steady-state of the PV-BES system during FRT can be analyzed. The proposed method is fully tested, and the results demonstrate that with the proposed method, the voltage and frequency security can be well guaranteed. © 2024 IEEE.

关键词： Renewable energy

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Day-Ahead Reporting Strategy of a Wind power Plant Based on CEEMDAN-IPSO-LSTM

Day-Ahead Reporting Strategy of a Wind Power Plant Based on ...

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2023 IEEE International Conference on power Science and Technology, ICPST 2023

作者： Lv, Dong Wang, Yuan Xu, Fei Zhang, Bing Wang, Hongye He, Li Zhu, Hui Li, Junhui Co. Ltd China Three Gorges Beijing China Tsinghua University State Key Laboratory of Power System and Power Generation Equipment Control and Simulation Beijing China

ISBN: (纸本)9798350311358

In the new power system, the output of new energy power stations has strong volatility, which leads to huge deviation assessment expenditure. Therefore, based on the improved data preprocessing algorithm and deep learning algorithm, a day-ahead reporting strategy of wind power plants considering error prediction characteristics is proposed. Firstly, considering the historical real output and prediction error, the historical data is decomposed based on the complete noise-assisted ensemble empirical mode decomposition algorithm (CEEMDAN). Then, the components are introduced into the long short-term memory (LSTM) optimized by the improved particle swarm optimization algorithm (IPSO), and the components are reconstructed to obtain the original prediction curve and the error prediction curve. Finally, the empirical distribution function is used to revise the prediction error curve, and the original prediction curve and the error prediction curve are superimposed to obtain the final day-ahead report curve. The correctness and feasibility of the reporting strategy are verified by simulation analysis. © 2023 IEEE.

关键词： Forecasting

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A Modulation Scheme of CLLC Converters for Bidirectional power Transfer with Reduced Switching Frequency Range

A Modulation Scheme of CLLC Converters for Bidirectional Pow...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wang, Leheng Chen, Huan Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing China

ISBN: (纸本)9798350376067

CLLC converters are broadly used in battery energy storage systems (BESS), where the capabilities of bidirectional operation and voltage regulation are desired. In the existing bidirectional power transfer scheme, pulse frequency modulation (PFM) is used to realize the reciprocal gain of forward and backward directions. It leads to not only large frequency modulation range but also asymmetric transfer characteristics. In this paper, a novel modulation scheme of CLLC converters for bidirectional power transfer is proposed. In the proposed backward power transfer scheme, the driving signals of primary and secondary-side switches are aligned at the falling-edge, which brings about symmetrical characteristics as forward scheme. This symmetry further ensures the identical switching frequency in both directions, which simplifies parameter design, minimizes switching frequency range, and improves efficiency. A 10-kW prototype is built and the experimental results validate the effectiveness of the proposed scheme, offering potential for cost-effective and efficient applications. © 2024 IEEE.

关键词： Switching frequency

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Enhancing deformation characteristics prediction of coarse-grained soils with time-series generative adversarial network-based data augmentation and pre-training

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Frontiers of Structural and Civil Engineering 2025年第3期19卷 396-410页

作者： Ying ZHANG Meng JIA Xuedong ZHANG Liping CAO Ziying AN Hongchao WANG Jinyu WANG School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China The State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China

Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and numerical simulations,face challenges like high costs,time consumption,and limited generalizability across different soils and *** address these limitations,this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes,aiming to obtain the axial strain(ε_(a))-volumetric strain(ε_(v))curve,which helps derive key mechanical parameters like cohesion(c),and elastic modulus(E).However,the limited data from triaxial tests poses challenges for training deep learning *** propose using a Time-series Generative Adversarial Network(TimeGAN)for data ***,we apply feature importance analysis to assess the quality of the numerical augmented data,providing feedback for improving the TimeGAN *** further enhance model performance,we introduce the pre-training strategy to reduce bias between augmented and real *** results demonstrate that our approach effectively predictscurve,with the mean absolute error(MAE)of 0.2219 and the R^(2) of *** analysis aligns with established findings in soil mechanics,underscoring the potential of our method in engineering applications.

关键词： coarse-grained soils deformation characteristics TimeGAN data augmentation pre-training

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Influence of Thermal Conductivity of Multi-scale Particles Epoxy Composites on High-Frequency Electrical Insulation Property 5

Influence of Thermal Conductivity of Multi-scale Particles E...

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5th IEEE International Conference on Dielectrics, ICD 2024

作者： Song, Yan-Hui Dang, Zhi-Min Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing China

ISBN: (纸本)9798350308976

Epoxy, with excellent electrical insulation properties, processing performance, and low price, is commonly used for transformers (110kV and below) and power electronic transformers (high-frequency transformers) as the primary insulating material. Since the thermal conductivity of epoxy resins is only 0.2 W/(m·K), the electrical insulation properties decline significantly at high frequency. Preparing epoxy composites with high thermal conductivity and excellent electrical insulation at high frequencies is a great challenge for industrial production. It is considered that SiO2 (9 eV), BN (5.9 eV), and Al2O3 (7.2-8.8 eV) are thermally conductive particles with good electrical insulation properties. The materials filled with different micron-scale particles have higher thermal conductivity than the nanofilled materials. In this study, 5 μm SiO2, 25 μm BN, and 40 μm Al2O3 were used as fillers, and the filler ratios were calculated according to the Dinger-Funk model, focusing on the effects of the variation of filler content on thermal conductivity and electrical insulation properties. The thermal conductivity of the epoxy composites improved with the increase of filler content up to 1.003 W/(m·K), while the AC breakdown field strength gradually decreased. However, the breakdown time at 20 kHz, 35 kV/mm bipolar square wave appeared to increase and then decrease with the increase of content. Therefore, the optimum content of fillers allows the preparation of efficient epoxy composites as insulating materials for power electronic transformers. This study provides ideas for designing thermally conductive composites operating at high frequency and high voltage fields. © 2024 IEEE.

关键词： epoxy resin high frequency bipolar square wave inorganic particle compounding thermal conductive electrical insulation

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Multi-Type Electric Vehicle Schedulability Modeling Method based on User Charging Behavior Characteristics 11

Multi-Type Electric Vehicle Schedulability Modeling Method b...

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11th China International Conference on Electricity Distribution, CICED 2024

作者： Xue, Yinjuan Chen, Qifang Cai, Haiqing Xia, Mingchao Gu, Haohan School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350368345

With the rapid proliferation of Electric Vehicles (EV s), effectively leveraging the regulation potential of EV charging loads has become a prominent issue. This paper proposes a multi-type EV schedulability modeling method based on the analysis of charging behavior characteristics of different user types. The model comprehensively considers the charging characteristics of various types of EV s, including private cars, ride-hailing vehicles, and taxis, utilizing a charging start-stop probability distribution model. Case studies demonstrate that this model can not only accurately reflect the charging demands of EV s but also provide valuable references for strategies involving EV fleet participation in grid regulation. © 2024 IEEE.

关键词： Charging stations

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The HEFT Algorithm Based on Node Merging 8

The HEFT Algorithm Based on Node Merging

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8th International Workshop on control Engineering and Advanced Algorithms, IWCEAA 2024

作者： Huang, Ying Lu, Yuanhong Zhang, Jie Zhang, Jingyue Guo, Haiping National Energy Power Grid Technology R&D Centre Guangzhou510663 China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou Guangzhou510663 China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China

ISBN: (纸本)9798331528881

The task scheduling problem in power systems is complex, especially when communication costs exceed computation costs. This paper proposes an improved HEFT algorithm with a node merging mechanism, merging nodes through hierarchical traversal to assign high-communication-cost tasks to the same processor, reducing communication overhead. Experiments with 499-dimensional and 1614-dimensional matrices from the power system show that the improved algorithm reduces completion time by 5ms and 2ms, respectively, compared to standard HEFT. © 2024 IEEE.

关键词： Scheduling algorithms

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FAULT DIAGNOSIS METHOD FOR DC DISTRIBUTION NETWORKS BASED ON SEMITENSOR PRODUCT BAYESIAN NETWORKS 6

FAULT DIAGNOSIS METHOD FOR DC DISTRIBUTION NETWORKS BASED ON...

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6th International Scientific and Technical Conference Relay Protection and Automation, RPA 2023

作者： Wang, He Li, Shiqiang Yu, Huanan Bian, Jing Wei, Bo Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

ISBN: (纸本)9798350382334

关键词： Bayesian networks

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Proof-of- Reliability Consensus Mechanism of Blockchain for Remote Operation of Relay Protection 7

Proof-of- Reliability Consensus Mechanism of Blockchain for ...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Zhang, Yongjie Cong, Wei Tang, Yi Li, Yudun Li, Congcong Shi, Fangfang Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350359558

Remote operation of protective setting value can enhance the adaptability of protection to real-time operating conditions. As centralized solutions, existing models face information security risks. The decentralized nature of blockchain is advantageous for addressing reliability issues in remote operations. This paper treats the control center and protection relay system sub-stations as nodes in blockchain, proposing a Proof-of- Reliability (PoRel) consensus mechanism based on node reliability. The paper first defines sub-station node types and classification criteria, achieving optimal sub-station node grouping based on 0-1 integer programming. Next, it provides a method for evaluating sub-station node reliability and outlines the consensus mechanism process. Finally, using the IEEE 39-bus system as an example, a blockchain system with 27 sub-station nodes is built using Python software, and the proposed consensus mechanism is simulated and verified. The results indicate that the proposed consensus mechanism improves efficiency, achieves decentralization, and helps ensure reliable relay setting remote operations. © 2024 IEEE.

关键词： Blockchain

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Real-time Optimal Energy Management of Combined Cooling, Heating and power systems Based on Knowledge conlusion 6

Real-time Optimal Energy Management of Combined Cooling, Hea...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： He, Chunjiang Wang, Yijian Xu, Yang Cui, Yang China Electric Power Research Institute State Key Laboratory of Power Grid Security and Energy Conservation Beijing China Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin China

ISBN: (纸本)9798350346671

Combined Cooling, Heating and power(CCHP) system is an important way to improve the efficiency of energy utilization and wind power consumption. In this case, increasing the operational flexibility of CCHP systems can help increase renewable energy consumption and reduce carbon emissions. So, it is necessary to provide real-time optimal energy management(OEM) for CCHP systems. In this paper. Firstly, a new training method is proposed for the LightGBM to improve the accuracy of real-time wind power prediction. In addition, to solve the nonlinear OEM of CCHP in real-time, a novel deep reinforcement learning algorithm-Soft Actor-Critic, is used in this paper. By combining the mechanism of CCHP with the reward function through knowledge fusion, the non-convex models can be translated into Markov Decision Processes(MDP), and then according to the Bellman optimal equation, the real-time optimized operation of the CCHP system is realized. Finally, the effectiveness of the proposed policy is verified by the actual operation data of a CCHP system in Northwest China. © 2023 IEEE.

关键词： Reinforcement learning

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