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Glacier area change and its impact on runoff in the Manas River Basin,Northwest China from 2000 to 2020

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Journal of Arid Land 2024年第7期16卷 877-894页

作者： WANG Tongxia CHEN Fulong LONG Aihua ZHANG Zhengyong HE Chaofei LYU Tingbo LIU Bo huang yanhao College of Water Conservancy&Architectural Engineering Shihezi UniversityShihezi 832000China Key Laboratory of Cold and Arid Regions Eco-Hydraulic Engineering of Xinjiang Production&Construction Corps Shihezi 832000China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China College of Sciences Shihezi UniversityShihezi 832000China

Understanding the distribution and dynamics of glaciers is of great significance to the management and allocation of regional water resources and socio-economic development in arid regions of Northwest *** this study,based on 36 Landsat images,we extracted the glacier boundaries in the Manas River Basin,Northwest China from 2000 to 2020 using eCognition combined with band operation,GIS(geographic information system)spatial overlay techniques,and manual visual *** further analyzed the distribution and variation characteristics of glacier area,and simulated glacial runoff using a distributed degree-day model to explore the regulation of runoff *** results showed that glacier area in the Manas River Basin as a whole showed a downward trend over the past 21 a,with a decrease of 10.86%and an average change rate of–0.54%/*** the increase in glacier scale,the number of smaller glaciers decreased exponentially,and the number and area of larger glaciers were relatively *** area showed a normal distribution trend of increasing first and then decreasing with *** 97.92%of glaciers were distributed at 3700–4800 m,and 48.11%of glaciers were observed on the northern and northeastern *** retreat rate of glaciers was the fastest(68.82%)at elevations below 3800 *** was a clear rise in elevation at the end of *** at different slope directions showed a rapid melting trend from the western slope to the southern slope then to the northern *** runoff in the basin showed a fluctuating upward trend in the past 21 a,with an increase rate of 0.03×10^(8) m^(3)/*** average annual glacial runoff was 4.80×10^(8) m^(3),of which 33.31%was distributed in the ablation season(June–September).The average annual contribution rate of glacial meltwater to river runoff was 35.40%,and glacial runoff accounted for 45.37%of the total runoff during the ablation *** addition,precipitation and glacial runoff had comp

关键词： glacier area glacial runoff climate change glacier boundary extraction distributed degree-day model Manas River Basin

WaveLines:towards effective visualization and analysis of stability in power grid simulation

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Frontiers of Computer Science 2021年第6期15卷 19-30页

作者： Tianye ZHANG Qi WANG Liwen LIN Jiazhi XIA Xiwang XU yanhao huang Xiaonan LUO Wenting ZHENG Wei CHEN The state key lab of CAD&CG Zhejiang UniversityHangzhou 310058China School of Computer Science and Engineering Central South UniversityChangsha 410083China The State Key Lab of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Closely related to the safety and stability of power grids,stability analysis has long been a core topic in the electric *** approaches employ computational simulation to make the quantitative judgement of the grid stability under distinctive *** lack of in-depth data analysis tools has led to the difficulty in analytical tasks such as situation-aware analysis,instability reasoning and pattern *** facilitate visual exploration and reasoning on the simulation data,we introduce WaveLines,a visual analysis approach which supports the supervisory control of multivariate simulation time series of power *** design and implement an interactive system that supports a set of analytical tasks proposed by domain experts and experienced *** have been conducted with domain experts to illustrate the usability and effectiveness of WaveLines.

关键词： stability visual analysis power grid simulation data

Framework for Artificial Intelligence Analysis in Large-scale Power Grids Based on Digital Simulation

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CSEE Journal of Power and Energy Systems 2018年第4期4卷 459-468页

作者： Yong Tang yanhao huang Hongzhi Wang Can Wang Qiang Guo Wei Yao State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China College of Computer Science at Harbin Institute of Technology Harbin 150001China College of Computer Science at Zhejiang University Hangzhou 310027China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China

The planning,design,operation,control and scientific research of power systems all require a variety of simulation *** power grid simulation analysis is a fundamental supporting technology of large-scale power *** power grid simulation analysis,in addition to simulation calculations,there are many links for analysis and decision-making,relying on *** introduction of advanced artificial intelligence technology provides a new method to improve the efficiency and accuracy of power grid simulation ***,the research of the related artificial intelligence technologies face a great deal of new challenges due to the complexity of the largescale power grid simulation data,including massive volumes,high dimensionality,strong coupling and complex *** a great deal of knowledge and experience need to be integrated in the process of *** order to deal with these challenges,based on the existing works,this paper focuses on the core scientific problem of artificial intelligence analysis and decision making related to the massive simulation results of large-scale power grids,and proposes an artificial intelligence analysis method framework for large-scale power grids based on digital simulation,which includes the power grid simulation analysis knowledge model with application method,the power grid simulation knowledge mining method and the artificial intelligence models with transfer learning ability of diversified grids as well as analyzing and calculation adjusting for largescale power grid simulation results,*** work is expected to open up a new technical approach for large-scale power grid simulation analysis and provide strong technical support for the safe and stable operation of large-scale power grids.

关键词： Artificial intelligence digital simulation power system analysis

Knowledge Model for Electric Power Big Data Based on Ontology and Semantic Web

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CSEE Journal of Power and Energy Systems 2015年第1期1卷 19-27页

作者： yanhao huang Xiaoxin Zhou China Electric Power Research Institute China

It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big *** modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system *** theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling *** on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is *** forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and *** shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big *** the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.

关键词： Electric power big data knowledge model ontology semantic web

Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition

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Frontiers of Information Technology & Electronic Engineering 2022年第3期23卷 382-397页

作者： Supaporn LONAPALAWONG Jiangzhe YAN Jiayu LI Deshi YE Wei CHEN Yong TANG yanhao huang Can WANG State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Mathematical Science Zhejiang UniversityHangzhou 310058China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China

Analyzing network robustness under various circumstances is generally regarded as a challenging *** against failure is one of the essential properties of large-scale dynamic network systems such as power grids,transportation systems,communication systems,and computer *** to the network diversity and complexity,many topological features have been proposed to capture specific system *** power grids,a popular process for improving a network’s structural robustness is via the topology ***,most of existing methods focus on localized network metrics,such as node connectivity and edge connectivity,which do not encompass a global perspective of cascading propagation in a power *** this paper,we use an informative global metric algebraic connectivity because it is sensitive to the connectedness in a broader spectrum of *** process involves decreasing the average propagation in a power grid by minimizing the increase in its algebraic *** propose a topology-based greedy strategy to optimize the robustness of the power *** evaluate the network robustness,we calculate the average propagation using MATCASC to simulate cascading line outages in power *** results illustrate that our proposed method outperforms existing techniques.

关键词： Network robustness Cascading failure Average propagation Algebraic connectivity Power grid

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基于本体理论的智能电网广义数据管理模型

电力系统自动化 2014年第9期38卷 114-118页

作者：黄彦浩周孝信中国电力科学研究院北京市100192

针对广义数据自动获取问题,研究了可用于智能化处理的广义数据管理模型。广义数据类型多样、结构复杂,管理模型通过建立不同广义数据间的内在联系达到将其纳入管理的目的,而对广义数据的存储、解析和利用则交由相应的应用系统负责。广... 详细信息

针对广义数据自动获取问题,研究了可用于智能化处理的广义数据管理模型。广义数据类型多样、结构复杂,管理模型通过建立不同广义数据间的内在联系达到将其纳入管理的目的,而对广义数据的存储、解析和利用则交由相应的应用系统负责。广义数据间的联系复杂多样,现有的电网知识模型不能满足对其建模、应用的要求。因此,以本体和语义网技术为基础,研究了该模型的逻辑基点、结构和各级构件,并讨论了其建模和应用方式,最后通过一个案例说明了该模型的使用效果。研究表明,该模型可用于电网多类型异构数据资源的管理和获取,并具备良好的可扩展性。

关键词：广义数据知识建模本体语义网

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中国司法改革年度报告(2019)

上海大学学报（社会科学版） 2020年第3期37卷 49-67页

作者：徐昕黄艳好北京理工大学法学院北京100081 湘潭大学法学院湖南湘潭411105

2019年,司法改革总体上是补充性的机制完善。法院、检察院、司法部公布各自的五年改革纲要,在推进合宪性审查工作、废除收容教育制度、完善民事和刑事证据规则、社区矫正立法、开展刑事辩护全覆盖试点等方面有所进展。但多数改革是对先... 详细信息

2019年,司法改革总体上是补充性的机制完善。法院、检察院、司法部公布各自的五年改革纲要,在推进合宪性审查工作、废除收容教育制度、完善民事和刑事证据规则、社区矫正立法、开展刑事辩护全覆盖试点等方面有所进展。但多数改革是对先前改革的落实和既有机制的完善。在改革框架确定的情况下,可通过建立政策指导调整司法、司法有效适度回应政策的良性互动机制,逐步改善司法政治生态,点滴推动法治。

关键词：司法改革司法与政策合宪性审查

基于双Q学习的考虑暂态稳定约束的电网薄弱线路辨识

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中国电机工程学报 2020年第8期40卷 2429-2440页

作者：曾令康姚伟艾小猛黄彦浩文劲宇强电磁工程与新技术国家重点实验室(华中科技大学电气与电子工程学院) 湖北省武汉市430074 中国电力科学研究院有限公司北京市海淀区100192

为了辨识故障后对电网暂态稳定性影响较大的薄弱线路,提出一种基于双Q学习(double Q-learning,DQL)的考虑暂态稳定约束的电网薄弱线路辨识方法。首先,将电网薄弱线路辨识问题转化成马尔克夫决策过程。接着,利用DQL智能体结合电网时域暂... 详细信息

为了辨识故障后对电网暂态稳定性影响较大的薄弱线路,提出一种基于双Q学习(double Q-learning,DQL)的考虑暂态稳定约束的电网薄弱线路辨识方法。首先,将电网薄弱线路辨识问题转化成马尔克夫决策过程。接着,利用DQL智能体结合电网时域暂态仿真计算,通过强化学习筛选出容易导致电网失稳的切线故障。最后,提出线路薄弱度指标,计算得到考虑暂态稳定约束的电网薄弱线路。所采用的DQL在Q学习的基础上增加了Q目标表,实现切线故障选择与评估的分离,并采用优先级采样的经验回放机制,能提升算法的稳定性与训练速度。10机39节点和16机68节点系统的仿真结果都表明,所提基于DQL算法的薄弱线路辨识方法,能通过较少的仿真次数,有效地辨识出考虑系统暂态安全稳定约束的薄弱线路。

关键词：薄弱线路辨识暂态稳定双Q学习强化学习优先级采样经验回放

基于SE-CNN和仿真数据的电力系统主导失稳模式智能识别

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中国电机工程学报 2022年第21期42卷 7719-7730页

作者：石重托姚伟黄彦浩汤涌文劲宇强电磁工程与新技术国家重点实验室(华中科技大学电气与电子工程学院) 湖北省武汉市430074 中国电力科学研究院有限公司北京市海淀区100192

故障后的主导失稳模式识别是电力系统仿真分析中的一项重要内容,用以指导制定在线应用的紧急控制措施策略表,但实际电网的功角失稳和电压失稳常交织在一起而难以判断。该文探索人工智能在主导失稳模式识别上的应用,提出一种深度学习的... 详细信息

故障后的主导失稳模式识别是电力系统仿真分析中的一项重要内容,用以指导制定在线应用的紧急控制措施策略表,但实际电网的功角失稳和电压失稳常交织在一起而难以判断。该文探索人工智能在主导失稳模式识别上的应用,提出一种深度学习的方法。该方法基于仿真所得的发电机功角和母线电压数据,直接识别出系统的稳定状态和主导失稳模式,即稳定、功角失稳或电压失稳。采用改进的卷积神经网络(convolutional neural network,CNN)模型实现这一智能方法,即通过引入挤压激励网络模块,显式建模CNN特征通道间的相关关系,自适应调整特征通道的权重,进一步提升CNN的效果。以中国电科院36节点系统和我国东北电网为例进行仿真,结果验证了所提主导失稳模式识别方法相比传统机器学习方法在有效性、准确性和适应性方面存在一定的优势。结果验证了基于人工智能的主导失稳模式识别的可行性和准确性。

关键词：电力系统仿真分析主导失稳模式功角稳定电压稳定挤压激励网络卷积神经网络

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