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2022 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Liu, Shuming Liu, Shuqi Wang, Qian Zuo, Zhou Wang, Tingting Luo, Bing Liang, Xidong Tsinghua University State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Beijing100084 China Csg Electric Power Research Institute Co. Ltd. Guangzhou510663 China The Natl. Eng. Res. Ctr. for Ultra High Voltage Eng. Technol. and Innov. Elec. Equipment Foundation China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

Composite insulators, as an important part of outdoor transmission lines, always suffer from various environmental stress, and the large-scale icing accretion problem is one of the dangerous threats. In this paper, superhydrophobic silicone rubber was prepared by surface coating method, and its anti-icing performance compared with pristine silicone rubber was investigated. The superhydrophobic surface can repel water droplets so that the droplets can be removed easily before freezing. For the sessile droplets, the freezing speed on the pristine silicone rubber is faster than the process on coated silicone rubber. After completing freezing, the deicing force on pristine silicone rubber is bigger than that on coated silicone rubber. In summary, the superhydrophobic silicone rubber showed more excellent performance in inhibiting ice accretion than the pristine silicone rubber, and it is a potential solution to the icing problem in the power system. © 2022 IEEE.

关键词： Electric power transmission

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An Equivalent Modeling Method for Integrated Hydro-Photovoltaic-Wind systems Based on Particle Swarm Optimization and Neural Networks

An Equivalent Modeling Method for Integrated Hydro-Photovolt...

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Intelligent Power and systems (ICIPS), International Conference on

作者： Ying Huang Yuanhong Lu Yanjun Zhao Yufeng Hu Jie Zhang Haiping Guo State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou 510663 China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou 510663 China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou 510663 China Southern Power Grid Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou 510663 China Southern Power Grid Guangzhou China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

The fault transient characteristics of power grids have undergone a fundamental transformation as a consequence of the advent of hybrid scale access to power grids by new energy sources. Nevertheless, the existing stand-alone equivalent model is unable to accurately characterise the fault transient characteristics of the hybrid light-water-wind electric field/cluster. Furthermore, the simulation computation of the stand-alone equivalent model is considerable in scale and the simulation efficiency is low. This paper proposes a particle swarm neural network-based clustered equivalent modelling method for a light-water-wind hybrid electric field. Firstly, a transient model of the light-water-wind hybrid electric field is established, and the characteristic influence factors of faults are constructed through theoretical analysis. These include the distance between the light-water-wind hybrid electric field and the point of common coupling (PCC), the input of current limiting measures on the DC side, the light, the water speed, the wind speed, and the reactive power at the outlet. Subsequently, the Euclidean distance is calculated for the set of characteristic influencing factors, and the initial centre of clustering of wind turbines is extracted based on the improved maximum-minimum distance method. Ultimately, the clustering equivalent modelling is accomplished through the utilisation of the aforementioned feature influencing factors and the classification initial centre as the training set. The simulation results show that compared with the single-machine equivalent model, The simulation error has been reduced from the order of magnitude of 10-2 p.u. to the order of magnitude of 10-4 p.u., while the simulation efficiency of this method is has been reduced from the order of magnitude of 10-2 s to the order of magnitude of 10-4 s.

关键词： Adaptation models Reactive power Power measurement Computational modeling Wind speed Robustness Power grids Electric fields Transient analysis Particle swarm optimization

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Capacity and Power Allocation Strategy of Energy Storage Participating in Grid Peak Shaving 5

Capacity and Power Allocation Strategy of Energy Storage Par...

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5th IEEE Conference on Energy Internet and Energy system Integration, EI2 2021

作者： Qi, Tengyun Xu, Fei Wu, Qiren Li, Yajing Liu, Jianping Li, He Wang, Yiming Li, Cuiping Li, Junhui China Three Gorges Corporation Beijing China Haidian District Beijing China Co. Ltd Beijing101100 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9781665434256

High penetration wind power grid with energy storage system can effectively improve peak load regulation pressure and increase wind power capacity. In this paper, a capacity allocation method of energy storage system under peak load regulation scenario is proposed. The upper model combines the investment cost, operation cost, arbitrage income, environmental income, and wind power grid benefits during the entire life cycle of the energy storage system, with the goal of maximizing the net income during the energy storage life cycle;the lower model optimizes the charging and discharging power of the energy storage system according to each energy storage configuration scheme in the upper modeling, and its objective is to minimize the standard deviation of system net load and waste wind power. Finally, under the conditions of local grid load and wind power data, verified the effectiveness of capacity allocation strategy. © 2021 IEEE

关键词： Economic analysis

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Optimization Study on the control Strategy of DC Line Fault Recovery for Yongren-Funing HVDC

Optimization Study on the Control Strategy of DC Line Fault ...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Yukun Zhu Libin Huang Shuyong Li Yihua Zhu Yantao Song Ren Liu State Key Laboratory of HVDC China Southern Power Grid Electric Power Research Institute 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 China Southern Power Grid Electric Power Research Institute Guangzhou China XJ Electric Co Ltd Xuchang China

The ±500kV Yongren-Funing HVDC transmission project is the first HVDC project whose sending and receiving ends are both located in the same province. When DC line fault happened under the condition of inter-communication fault between the pole control systems of the rectifier and inverter stations, the HVDC system cannot be blocked because of the frequent repeating actions of the DC line fault recovery in the inverter station. In order to solve the problem, an optimal control strategy of direct blocking HVDC system was proposed to solve the above issue. The optimization control strategy was verified by RTDS simulation. The simulation results showed that the proposed optimization control strategy of the DC line fault recovery could solve the above problems effectively. The proposed strategy has already been implemented on site permanently, which has great significance for the HVDC/UHVDC transmission projects in operation currently.

关键词： HVDC transmission simulation Capacitors Rectifiers Optimal control system integration Inverters

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Anthropogenic monoterpenes aggravating ozone pollution

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National Science Review 2022年第9期9卷 178-185页

作者： Haichao Wang Xuefei Ma Zhaofeng Tan Hongli Wang Xiaorui Chen Shiyi Chen Yaqin Gao Ying Liu Yuhan Liu Xinping Yang Bin Yuan Limin Zeng Cheng Huang Keding Lu Yuanhang Zhang State Key Joint Laboratory of Environmental Simulation and Pollution Control State Environmental Protection Key Laboratory of Atmospheric Ozone Pollution ControlCollege of Environmental Sciences and Engineering Peking University School of Atmospheric Sciences Sun Yat-sen University Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary Key Laboratory of Tropical Atmosphere–Ocean System Ministry of Education Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Institute of Energy and Climate Research IEK-8:TroposphereForschungszentrum Jülich Gmb H State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Complex Shanghai Academy of Environmental Sciences Institute for Environmental and Climate Research Jinan University

Monoterpenes have been known to have a critical influence on air quality and climate change through their impact on the formation of fine *** we present field evidence that monoterpene oxidations largely enhanced local ozone production in a regional site in eastern *** observed monoterpene was most likely from biomass burning rather than biogenic emissions,as indicated by the high correlation with CO at night-time,and the observed ratio of these two species was consistent with previously determined values from biomass burning *** monoterpene oxidations were determined experimentally based on direct radical measurements,leading to a daily ozone enhancement of 4-18 parts per billion by volume（ppb）,which was 6%-16% of the total ozone production,depending on the speciation of *** demonstrates that the previously overlo oked anthropogenic monoterpenes make an important contribution to 03 production in eastern *** role could possibly be important at similar locations across China and other parts of the world that are characterized by massive emissions,especially where there are high *** results highlight that anthropogenic monoterpenes should be taken into account when proceeding with the coordinated mitigation of O3and particulate matter pollution.

关键词： monoterpenes ozone pollution biomass burning anthropogenic emissions radical chemistry

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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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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on Energy Storage system

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Shijiazhuang Gaocheng District Electric Supply Company No. 76 Changsheng South Street Gaocheng District Shijiazhuang052160 China

Large wind farms connected to the grid through MMC-HVDC systems can cause overloading of the non-faulty converter station after a DC fault. This short communication proposes a system fault ride-through method by coordinating and controlling the energy storage system, the non-faulty converter station and the wind farm to jointly consume the unbalanced power during the fault. The model is created in the PSCAD simulation platform, and the correctness and effectiveness of the method are verified by simulation. © 2023, The Authors. All rights reserved.

关键词： Energy storage

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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on Energy Storage system

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control& Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China State GridShijiazhuang Gaocheng District Electric Supply Company No. 76Changsheng SouthStreet Gaocheng District Shijiazhuang052160 China

关键词： Energy storage

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Ultra-Short-Term Multi-Step Prediction of Wind Power Based on ModernTCN and Multi-Task Learning

Ultra-Short-Term Multi-Step Prediction of Wind Power Based o...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Mao Yang Xiangyu Li Zifen Han Yong Sun Xiqiang Chang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China State Grid Gansu Electric Power Company Gansu China State Grid Jilinsheng Economic And Technological Research Institute Jilin China State Grid Xinjiang Electric Power CO. LTD Xinjiang China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

The intermittent and fluctuating nature of wind energy makes it difficult to accurately predict wind power, leading to low penetration in the wind power grid. To address this problem, this paper designs ultra-short-term multi-step prediction of wind power based on ModernTCN and multi-task learning to improve the prediction accuracy. Firstly, ModernTCN's ability to have a large receptive field and capture cross-variate timing dependencies is exploited to obtain multivariate timing feature information and establish a multi-step mapping of input features to power. Then it is inputted into the MMoE multi-task learning method, and the prediction results are corrected to obtain the ultra-short-term prediction of wind power using multiple expert networks and gating units. The experimental results show that the method proposed in this paper can effectively improve the ultra-short-term prediction accuracy of wind power, and can provide a favourable reference for the scheduling centre to adjust the power generation plan in real-time response to the power demand.

关键词： Renewable energy sources Accuracy Wind energy Wind power generation Predictive models Multitasking Feature extraction Real-time systems Timing Wind forecasting

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Visualization of CNN Transient Voltage Classification Based on Feature Recognition and Enhancement

Visualization of CNN Transient Voltage Classification Based ...

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Sustainable Power and Energy Conference (iSPEC)

作者： Zhendong Li Yutian Liu Jun An Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9781665414401

The deep learning algorithm represented by CNN has achieved remarkable results in complex feature extraction, classification and regression. Since the CNN adopts an end-to-end training process, the carrier of acquisition knowledge is a high-dimensional non-linear function, which is hard to understand by humans. Therefore, it is difficult to be widely used in online decision-making engineering scenarios such as the fast assessment of power system transient voltage stability. In this paper, the key input elements extracted by CNN are identified by Grad-CAM, and the key features captured by CNN in the assessment process are enhanced based on AM, so as to finally obtain the visual expression of key areas and variables of the original input data. Besides, a sample construction method based on single line diagram is proposed to improve the visualization effect of Grad-CAM and AM. simulation results and analyses of IEEE 39bus test system demonstrates the effectiveness of the proposed method.

关键词： Training Visualization simulation Data visualization Voltage Power system stability Feature extraction

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