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Improved Insulation Properties of Polypropylene With Highly Orientated Lamellar Induced by NonIsothermal Shear Stress

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IET Nanodielectrics 2025年第1期8卷

作者： Huang, Lei Song, Yan-Hui Yin, Li-Juan Zhong, Shao-Long Dang, Zhi-Min Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China ZhongYuan Electric Laboratory Xuchang China

Polypropylene plays an irreplaceable role in electrical insulation and electrostatic capacitors and has been extensively studied with its microstructure and insulation properties optimised for high-power density miniaturised devices. The extrinsic modifiers or industrial upgrading have not yet achieved a remarkable breakthrough in either concise theory or comprehensive performance. Here, we develop a nonisothermal shear process on melt to regulate the nucleation density and the lamellar orientation along the surface and reveal pore defects in disorder boundaries between spherulites as the microscopic nature of the property bottleneck. The high-rated shear process induces a crystallographic surface texture of (1 1 0) planes from which the elimination of micro-defect benefits. Our high-rated shear strategy achieves an excellent dielectric strength of 573.7 MV/m with an increase of 33% and an improved insulation resistance by 130% at 100 MV/m with the minimised absorption of injected current. © 2025 The Author(s). IET Nanodielectrics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Crystal orientation

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PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency

arXiv

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arXiv 2025年

作者： Dong, Xintong Yuan, Zhengyi Lin, Jun Dong, Shiqi Tong, Xunqian Li, Yue College of Instrumentation and Electrical Engineering Jilin University Changchun130026 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Northeast Electric Power University Jilin132012 China College of Communication Engineering Jilin University Changchun130012 China

Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observed waveforms. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Consequently, various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process. This study presents a simple yet effective training framework that is independent of dataset reliance and requires only moderate pre-training on a simple initial model to stabilize network outputs. During the transfer learning phase, the conventional FWI gradients will simultaneously update both the neural network and the proposed adaptive residual learning module, which learns the residual mapping of large-scale distribution features in the network’s output, rather than directly fitting the target mapping. Through this synergistic training paradigm, the proposed algorithm effectively infers the physically-informed prior knowledge into a global representation of stratigraphic distribution, as well as capturing subtle variations in inter-layer velocities within local details, thereby escaping local optima. Evaluating the method on two benchmark models under various conditions, including absent low-frequency data, noise interference, and differing initial models, along with corresponding ablation experiments, consistently demonstrates the superiority of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Stratigraphy

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The Day-ahead Scenario Generation Method for New Energy Based on an Improved Conditional Generative Diffusion Model

arXiv

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arXiv 2025年

作者： Wang, Changgang Liu, Wei Cao, Yu Liang, Dong Li, Yang Mo, Jingshan Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin Province Jilin132012 China School of Electrical Engineering Northeast Electric Power University Jilin Province Jilin132012 China Jilin Power Supply Company Substation Secondary Maintenance Center Jilin Province Jilin132012 China

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability. © 2025, CC BY-NC-ND.

关键词： Markov chains

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Unraveling Plasma Characteristics and Breakdown Mechanism in Nanosecond Pulsed Dielectric Barrier Discharge

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Li, Mengbo Chen, She Cui, Yingzhe Wang, Qiao Liu, LinLin Zhou, Xuan Zhuang, Chijie Wang, Feng Hunan University College of Electrical and Information Engineering Changsha410082 China Department of Electrical Engineering Tsinghua University Beijing100084 China China Electric Power Planning & Engineering Institute Beijing100120 China School of Electric Power South China University of Technology Guangzhou510641 China State Key Laboratory of Disaster Prevention & Reduction for Power Grid Changsha University of Science & Technology Changsha410114 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

Nanosecond-pulsed dielectric barrier discharges (DBDs) have gained significant attention for its promising applications in various fields. More systematic investigations are necessary to better understand the fundamental processes of pulsed DBD. In this study, the characteristics and the initial breakdown mechanism of atmospheric nanosecond-pulsed DBD are investigated. The Electric Field-Induced Second Harmonic (E-FISH) technique is employed to investigate the electric field within the discharge gap. Additionally, the effects of pulse amplitude and pulse repetition rate on discharge characteristics, particularly the electric field, are analyzed. The results indicate that an increase in pulse amplitude leads to higher breakdown currents and a more pronounced reverse electric field peak, whereas the pulse repetition rate has a minimal effect on discharge behavior. A two-dimensional fluid model is developed to simulate discharge dynamics and analyze the spatiotemporal evolution of particles. Based on both experimental and simulation results, the discharge process is characterized by three distinct stages: Stage I (discharge breakdown), Stage II (surface charge accumulation), and Stage III (surface charge neutralization). The breakdown mechanism is further elucidated by examining the role of residual charges near the dielectric layer, which enhance the local electric field and facilitate the generation of secondary electrons, leading to ionization and subsequent discharge establishment. © 2025 IEEE.

关键词： Dielectric barrier discharge

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Effect of ZnO Catalyst on the Degradation of SF6 by DBD Plasma

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Li, Yalong Tan, Huiyu Gao, Peng Zhang, Xiaoxing Wang, Dibo Fu, Mingli Zhuo, Ran Yu, Lei Liu, Wei Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China CSG Electric Power Research Institute Co. Ltd Guangzhou510663 China State Grid Anhui Electric Power Company Electric Power Research Institute Hefei230022 China

Sulfur hexafluoride (SF6) is widely used in gas-insulated equipment due to its excellent electrical properties. However, SF6 is a potent greenhouse gas, and with the implementation of the "dual carbon" strategy, reducing SF6 emissions has become imperative. This paper analyzes the interaction between SF6 and the ZnO surface through simulation calculations and studies the effects of ZnO-filled systems on SF6 degradation by DBD through experiments. The results demonstrate that the charge transfer of individual fluorine atoms is relatively weak, with no significant charge transfer occurring before and after the adsorption of each fluorine atom. Furthermore, after adsorption on the ZnO surface, the SF6 molecule does not undergo noticeable deformation, indicating that the adsorption of SF6 on the ZnO surface is physical adsorption. Under the conditions of a 2% SF6 concentration, a gas flow rate of 150 mL/min, and an input power of 90 W, the SF6 degradation rate and equivalent capacitance with ZnO filling were found to be 96.6% and 79.31 pF, respectively. At an input power of 80W, the energy yield reached a maximum value of 12.41 g/kWh, and the main decomposition products of SF6 were SO2, SO2F2, and SOF2. This study provides a theoretical basis for the efficient degradation and harmless treatment of SF6 © 1994-2012 IEEE.

关键词： Adsorption

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LiFePO4 Battery SOC Estimation under OCV-SOC Curve Error Based on Adaptive Multi-Model Kalman Filter

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IEEE Transactions on Transportation Electrification 2025年

作者： Paizulamu, Daniyaer Cheng, Lin Xu, Helin Zhuang, Yingrui Qi, Ning Ci, Song Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Columbia University Department of Earth and Environmental Engineering New YorkNY10027 United States

LiFePO4 batteries are widely used in electric vehicles and energy storage systems due to long cycle life and high safety performance. However, the open circuit voltage-state of charge curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) estimation highly sensitive to OSC error, which arises due to aging and temperature. To address this, we propose an SOC estimation method that accounts for error in OSC. First, we establish battery equivalent circuit model and introduce a parameters identification algorithm based on adaptive recursive least squares. Next, we derive the relationship between the innovation's cross-correlation matrix/auto-correlation matrix of the Kalman filter and the OSC error. We then develop an adaptive multi-model Kalman filter, which dynamically adjusts the measurement model parameters of each filter based on the sign of the OSC error. By assigning a probability to each filter according to its predicted voltage distribution function, the optimal filter is selected. The proposed method is tested under various OSC error types and operating conditions. Results demonstrate that the proposed method achieves high accuracy and robustness, with root mean square error of less than 0.03, also offers significant advantages in computational efficiency, interpretability, and generalizability. © 2015 IEEE.

关键词： Distribution functions

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Performance-Aware control of Modular Batteries For Fast Frequency Response

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IEEE Transactions on Sustainable Energy 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Massachusetts Institute of Technology Lab for Information and Decision Systems CambridgeMA02139 United States

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance. © 2010-2012 IEEE.

关键词： Battery management systems

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A Quasi-Parallel Resonant Switched Capacitor DC-DC Converter with Wide Input Voltage Range Regulation and Enhanced Dynamic Performances

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IEEE Journal of Emerging and Selected Topics in power Electronics 2025年

作者： Qi, Jingjing Wu, Xuezhi Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Beijing Jiaotong University National Active Distribution Network Technology Research Center Beijing100044 China

Wide-range and high-efficiency DC-DC converters are in high demand for various applications, such as distributed systems, electric vehicles, and DC buildings. Resonant switched capacitor converters (ReSCC) can achieve soft switching, have small passive device capacity, and exhibit low voltage stress, but the voltage ratio is difficult to be regulated continuously. In this paper, a quasi-parallel resonant switched capacitor DC-DC converter is proposed to satisfy the design requirements, which incorporates a ReSCC with soft-switching and variable voltage ratio, as well as a Buck converter. In addition, a simplified optimal trajectory controller for ReSCC is investigated, which can be combined with PI controller of Buck converter to enhance the load transient of the proposed converter. Moreover, the input voltage feedforward control is implemented with the optimized feedforward coefficient to suppress the input voltage disturbance. The proposed converter can achieve high efficiency and rapid dynamic response over a wide conversion range. The correctness of the theoretical analyses is verified by a 36V∼60V-5V/20A prototype. © 2013 IEEE.

关键词： Capacitor bank

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Fuzzy optimal scheduling of hydrogen-integrated energy systems with uncertainties of renewable generation considering hydrogen equipment under multiple conditions

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Applied Energy 2025年 393卷

作者： Song, Jianzhao Wang, Na Zhang, Zhong Wu, Hao Ding, Yi Pan, Qingze Pan, Xingzuo Shui, Siyuan Chen, Haipeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Gansu Branch of Luneng New Energy Group Co. Ltd. Lanzhou730000 China Lanzhou Power Supply Company State Grid Gansu Electric Power Co. Ltd. Lanzhou730000 China TBEA Shenyang Transformer Group Co. Ltd. Shenyang110144 China Northeast Electric Power University Jilin132012 China

Developing hydrogen-integrated energy systems (HIES) represents a cutting-edge strategy for harnessing renewable energy (RE). However, the inherent unpredictability and variability of RE significantly increase the operational uncertainty of HIES, which leads to severe energy curtailment issues when HIES integrates large-scale RE, necessitating greater operational flexibility in the system. To address the challenges, this paper proposes a fuzzy optimal scheduling approach for HIES that considers the hydrogen equipment under multiple operating conditions. The analysis begins by examining the operational mechanisms of hydrogen equipment. Subsequently, a multi-conditions model is developed for electrolyzers, while a reserve model is established for hydrogen fuel cells. Furthermore, the fuzzy chance constraint (FCC) is employed to quantify the uncertainty of RE generation. An integrated demand response mechanism is implemented, incorporating human thermal comfort and building thermal inertia. Finally, a fuzzy optimization scheduling model for HIES is constructed to minimize the total operating costs. The crisp equivalent of FCC is derived to solve this model, thereby transforming the scheduling model based on fuzzy chance-constrained programming into a solvable mixed-integer programming model. The simulation results indicate that the proposed scheduling method can reduce the overall costs of the HIES by 17.98 % and increase the RE accommodation rate by 19.67 %, validating the effectiveness of the method in enhancing the operational flexibility of the HIES. In addition, this study achieves a balance between economic efficiency and reliability, offering a better economy, lower energy curtailment rates, and faster decision-making times compared to robust optimization, scenario analysis, and other common fuzzy optimization methods. © 2025

关键词： Risk perception

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Sharing energy in wider area: A two-layer energy sharing scheme for massive prosumers

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Applied Energy 2025年 392卷

作者： Su, Yifan Yang, Peng Kang, Kai Wang, Zhaojian Qi, Ning Liu, Tonghua Liu, Feng State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing400044 China School of Electronics and Information Xi'an Polytechnic University Xi'an710048 China State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Earth and Environmental Engineering Columbia University New YorkNY10027 United States

The popularization of distributed energy resources transforms end-users from consumers into prosumers. Inspired by the sharing economy principle, energy sharing markets for prosumers are proposed to facilitate the utilization of renewable energy. This paper proposes a novel two-layer energy sharing market for massive prosumers, which can promote social efficiency by wider-area sharing. In this market, there is an upper-layer energy sharing market (U-ESM) in the distribution system and numerous lower-layer energy sharing markets (L-ESMs) in local communities. Prosumers in the same community share energy with each other in the L-ESM, which can be uncleared. The energy surplus and shortage of L-ESMs are cleared in the U-ESM. Thanks to the wider-area two-layer structure, the market outcome is near-social-optimal in large-scale systems. However, the proposed market forms a complex mathematical program with equilibrium constraints (MPEC). To solve the problem, we propose an efficient and hierarchically distributed bidding algorithm. The proposed two-layer market and bidding algorithm are verified on the IEEE 123-bus system with 11,250 prosumers, which demonstrates the practicality and efficiency for large-scale markets. © 2025 Elsevier Ltd

关键词： Game theory

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