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Theoretical and experimental studies on the self-healing properties of metallised capacitor films under multiple stresses

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High Voltage 2024年第6期9卷 1347-1356页

作者： Zhiyuan Wu Shangshu Liu Tong Tong Shaolong Zhong Jiantao Wang Hongbin Qi Zhimin Dang Wei Wang Beijing Key Laboratory of High Voltage and Electromagnetic Compatibility North China Electric Power UniversityBeijingChina State Key Laboratory of Power System Operation and Control Tsinghua UniversityBeijingChina

Metallised film capacitors,for the most important merits is the excellent self-healing property,have significant electrical insulation *** essential factors affecting the self-healing properties of metallised polypropylene film capacitors(MPPFCs)are first analysed,and a self-healing performance characterisation test platform for metallised polypropylene capacitor films was *** the voltage/current waveforms and discharge patterns of the self-healing process under multiple stresses including temperature,voltage and inter-layer pressure were recorded and *** correlation between self-healing performance characterisation and the carbon that appeared on the surface of the dielectric layer during the arc extinction time is ***,by virtue of an equivalent dynamic circuit model,the influence of square resistance and the equivalent capacitance of the power grid on self-healing has been theoretically studied.

关键词： films stresses film

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Two-stage Optimal Scheduling of Community Integrated Energy system Considering operation Sequences of Hydrogen Energy Storage systems

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Journal of Modern power systems and Clean Energy 2025年第1期13卷 276-288页

作者： Wei Kong Kai Sun Jinghong Zhao School of Electrical Engineering Naval University of EngineeringWuhanChina Department of Electrical Engineering State Key Laboratory of Power System Operation and ControlTsinghua UniversityBeijingChina

The hydrogen energy storage system(HESS)integrated with renewable energy power generation exhibits low reliability and flexibility under source-load *** address the above issues,a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems(CIESs)with power to hydrogen and heat(P2HH)*** aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for ***,the refined operation model of HESS is established,and its operation model is linearized according to the operation domain of HESS,which simplifies the difficulty in solving the optimization problem under the premise of maintaining high approximate ***,considering the flexible start-stop of alkaline electrolyzer(AEL)and the avoidance of multiple energy conversions,the operation sequences of HESS are ***,a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established,and the model is simulated and verified using the source-load prediction data of typical days in each *** simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14%while maintaining similar operating cost to the day-ahead economic optimal ***,by formulating the operation sequences of HESS,the operating cost of CIES is reduced by up to about 4.4%.

关键词： Community integrated energy system(CIES) hydrogen storage operation sequence two-stage optimal scheduling

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Generating High-Temperature Polymer Dielectrics with a Graph Convolution and Reinforcement Learning Framework

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Journal of Physical Chemistry C 2025年第22期129卷 10020-10026页

作者： Liu, Di-Fan Dong, Wen-Zhuo Wang, Xin-Jie Zhong, Shao-Long Dang, Zhi-Min State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China

Generating molecular structures with certain desired properties is a fundamental problem for material science research. Deep learning models have demonstrated big potential to identify novel materials by exploring large chemical spaces efficiently. Here, we demonstrate the generation of hypothetical polymers with a high glass-transition temperature (Tg) and a wide band gap (Eg) using a general graph convolutional network-based model through reinforcement learning. Two forward property prediction models on Tg and Eg are employed to provide domain-specific rewards. We further incorporate prior knowledge in the form of adversarial rewards given by a generative adversarial network framework via a data set of exemplar polymer structures. The domain-specific rewards and adversarial rewards are optimized in a reinforcement learning framework through policy gradient methods. Rules of chemistry are incorporated in the molecule generation environment as hard constraints. The generation process is guided to generate promising high-temperature polymer dielectrics for energy storage applications by property targeting and constrained property optimization. This work demonstrates potential for exploring large chemical spaces for the discovery of polymers with desirable properties. © 2025 American Chemical Society.

关键词： Deep reinforcement learning

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Low-Carbon Dispatching for Virtual power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value

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Journal of Modern power systems and Clean Energy 2024年第2期12卷 346-358页

作者： Hongchao Gao Tai Jin Guanxiong Wang Qixin Chen Chongqing Kang Jingkai Zhu State Key Laboratory of Power System Operation and Control Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Sichuan Energy Internet Research Institute Tsinghua University Chengdu 200016China Power Dispatching and Control Center of Shanghai Municipal Electric Power Company Shanghai 200122China IEEE

The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor *** address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and *** on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system ***,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both *** achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear ***,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and ***,the simulation verification based on the IEEE 33-bus DN is *** simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to ***,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

关键词： Distributed energy storage virtual power plant(VPP) spatiotemporal distribution low-carbon dispatching

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Research on Temperature Correction of Infrared Image based on Multi-emissivity of Substation Equipment 3

Research on Temperature Correction of Infrared Image based o...

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3rd International Conference on power systems and Electrical Technology, PSET 2024

作者： Dong, Jialong Han, Sheng Han, Xiaoqing Guo, Wei Taiyuan University of Technology Shanxi Key Laboratory of Power System Operation and Control Taiyuan China

ISBN: (纸本)9798350353204

Infrared thermal imaging technology is widely used for the inspection of substation equipment. Emissivity is necessary to calculate the temperature for infrared cameras, which varies with the different parts of substation equipment. Therefore, this paper proposes a multi-emissivity temperature correction method to obtain more accurate surface temperatures of substation equipment. Firstly, the paper explores a local parts recognition model for common substation equipment. Then, it studies the emissivity correction parameters for different parts. Finally, multi-emissivity is applied for temperature correction on various equipment parts in every infrared image. Test shows that the method can reduce temperature error than normal infrared cameras. © 2024 IEEE.

关键词： Temperature indicating cameras

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A data-driven topology identification method for low-voltage distribution network 3

A data-driven topology identification method for low-voltage...

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3rd International Conference on power systems and Electrical Technology, PSET 2024

作者： Shi, Shuo Han, Xiaoqing Li, Tingjun Zhang, Hao Yan, Hongbo Taiyuan University of Technology Shanxi Key Laboratory of Power System Operation and Control Taiyuan China

ISBN: (纸本)9798350353204

Correctly identifying the topology of low-voltage distribution grid is the key to ensure the safe and stable operation of the power system, and it is also the basis for realizing the loss analysis and fault diagnosis of the lines in the station area. Aiming at the situation that the data collected by smart meters may be missing, this paper proposes a data-driven method based on the topology identification of low-voltage distribution grids. The method firstly fills in the missing values and normalizes them according to the voltage time series data collected by smart meters. Second, the K-means clustering algorithm is used to classify and determine the upstream and downstream relationship of each user in a class in the distribution grid based on the magnitude of voltage amplitude. Then the correlation between nodes is calculated using Pearson correlation coefficient to calibrate the correctness of the topology. Finally, the feasibility of the proposed method is verified by simulation analysis of the IEEE33 node arithmetic example. © 2024 IEEE.

关键词： Network topology

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Deep Reinforcement Learning-based Multi-Scenario Multi-Scale Economic Scheduling for Integrated Energy systems 6

Deep Reinforcement Learning-based Multi-Scenario Multi-Scale...

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6th International Conference on Energy systems and Electrical power, ICESEP 2024

作者： Li, Guangxi Zhao, Qingsheng Han, Xiaoqing Li, Tingjun Taiyuan University of Technology Shanxi Key Laboratory of Power System Operation and Control Taiyuan China

ISBN: (纸本)9798350353563

The integrated energy system (IES) with multi-energy coupling and gradient utilization has become an important means to help realize the goal of 'double carbon'. The optimal operation of IES is a nonlinear, nonconvex and complex problem. With the gradual increase of intermittent energy and load uncertainty, traditional scheduling methods are limited to fixed physical models and parameter settings, which make it difficult to quickly and accurately obtain optimal scheduling strategies. In this paper, we propose a distributed proximal policy optimization (DPPO) algorithm to train the IES optimal scheduling model offline and solve the optimization online, which transforms the physical scheduling problem into a reinforcement learning sequential decision-making problem, and adaptively learns the scheduling policy through continuous interaction between the agent and the integrated energy system environment, and the multi-threaded structure enhances the exploration ability of the reinforcement learning agent in the complex environment. Example simulations show that online applications do not need to rely on accurate modeling of the physical model of the IES to achieve fast scheduling decisions in multiple scenarios and scales. © 2024 IEEE.

关键词： Deep reinforcement learning

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Modeling method of photovoltaic power generation grid connection based on particle swarm optimization neural network

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Energy Informatics 2024年第1期7卷 88页

作者： Zhang, Jie Lu, Yuanhong Huang, Libin Guo, Haiping Tu, Liang State Key Laboratory of HVDC China Southern Power Grid Guangzhou510000 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China Southern Power Grid Guangzhou510000 China CSG Key Laboratory for Power System Simulation China Southern Power Grid Guangzhou510000 China

Aiming at the complex structure, numerous equipment, intricate control and protection logic, as well as the existence of numerous unmodeled dynamics and black-box device models in photovoltaic (PV) grid-connected systems, a modeling method based on Particle Swarm Optimization Neural Network (PSO-NN) is proposed to address the inability of pure mechanism models to accurately simulate their operational dynamics. Utilizing the differences in active power response waveforms under Voltage-Frequency (Vf) control, power-Reactive power (PQ) control, and Droop control as criteria for control strategy identification, a PSO-NN model is constructed for PV grid-connected systems, with inputs comprising temperature, humidity, light intensity, voltage, and frequency disturbances, and outputs being active and reactive power. To validate the model's effectiveness, a PV grid-connected system model is built in a self-developed simulation software and connected to an IEEE 14-bus distribution network for simulation verification. The results demonstrate that the proposed PV grid-connected model can effectively identify the types of Vf control, PQ control, and Droop control strategies, and accurately reflect the dynamic response characteristics of active and reactive power under various voltage and frequency disturbances. © The Author(s) 2024.

关键词： Particle swarm optimization (PSO)

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Identifying faults in UHVDC transmission systems using DAE and ensemble learning 8

Identifying faults in UHVDC transmission systems using DAE a...

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2024 8th International Conference on Hydrodynamics, Energy and Electric power system, HEEPS 2024

作者： Guo, Yiheng Zhao, Qingshen Liang, Dingkang Shanxi Key Laboratory of Power System Operation and Control Taiyuan University of Technology Taiyuan030024 China

The current fault identification technique for the ultra high voltage direct current transmission system has a low success rate, the process of extracting feature qualities is complex, and it is difficult to identify high resistance grounding. This research provides a defect evaluation method for UHVDC transmission system using Deep Auto Encoder (DAE) and integrated learning. Firstly, the fault voltage signal is feature extracted using DAE, and then, a series of obtained features are feature screened by XGBoost algorithm and combined with an integrated classifier consisting of Multi-Layer Perceptron (MLP), K Nearest Neighbor (KNN), and Random Forest (RF) classifiers to identify the UHVDC faults. Finally, ±800kV UHVDC transmission line models are established in MATLAB/Simulink simulation software to simulate different types of faults. The study's findings demonstrate that the fault diagnostic method introduced in this research may significantly enhance the poor detection success rate of current fault diagnostics methods and is more resilient to excessive resistance. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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A Simple Open-loop Soft Startup Method Based on operation Mode Analysis for LCC Converters Used in High-voltage DC power Supply 10

A Simple Open-loop Soft Startup Method Based on Operation Mo...

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

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

ISBN: (纸本)9798350351330

LCC converters incorporate the stray parameters of high frequency transformer and are commonly employed as the initial stage of high-voltage DC power supplies, enhancing both efficiency and power density. However, the significant overshoot that occurs during the startup phase of high-voltage DC power supply poses considerable difficulties for the circuit. In this article, a simple open-loop soft startup control method is proposed: First, the operation mode analysis method is used to accurately solve the time-domain model of the LCC converter under different duty cycles in continuous current mode (CCM). Second, an open-loop variable duty cycle soft startup control strategy from the energy transmitted view is derived. Finally, soft startup process corresponding to different startup time requirements are verified on a 200V/20kV experimental prototype. © 2024 IEEE.

关键词： Time domain analysis

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