1.国家电力调度中心(国家电网),北京 100031
2.中国电力科学研究院有限公司,北京 100192
3.国网天津市电力公司,天津 300010
4.国网电力科学研究院有限公司,南京 211100
5.国网北京市电力公司,北京 100032
6.哈尔滨工业大学计算学部,哈尔滨 150006
[ "陶洪铸1973-,男,教授级高工主要研究方向为调度自动化E-mail:tao-hongzhu@sgcc.com.cn" ]
[ "王佳琪1990-,女,工程师研究方向为电力调度自动化E-mail:wangjiaqi0277@163.com" ]
收稿:2024-06-17,
修回:2024-10-25,
纸质出版:2024-11-30
移动端阅览
陶洪铸,王佳琪,陈建,等.基于调控云的人工智能资源多级共享框架与关键技术[J].新型电力系统,2024,02(4):371-385.
TAO Hongzhu,WANG Jiaqi,CHEN Jian,et al.Multi Level Sharing Framework and Key Technologies for Artificial Intelligence Resources Based on Dispatching and Control Cloud[J].New Type Power Systems,2024,02(04):371-385.
陶洪铸,王佳琪,陈建,等.基于调控云的人工智能资源多级共享框架与关键技术[J].新型电力系统,2024,02(4):371-385. DOI: 10.20121/j.2097-2784.ntps.N20240046.
TAO Hongzhu,WANG Jiaqi,CHEN Jian,et al.Multi Level Sharing Framework and Key Technologies for Artificial Intelligence Resources Based on Dispatching and Control Cloud[J].New Type Power Systems,2024,02(04):371-385. DOI: 10.20121/j.2097-2784.ntps.N20240046.
近年来人工智能技术在电网调控领域中的应用不断发展,随着调控云平台建设,相关业务对人工智能资源的多级共享提出了强需求。为此提出了一种基于调控云架构的人工智能资源多级共享框架,实现了数据样本集、算法模型、强化学习环境仿真器等资源在多级调控云上集成、存储、调用的全过程管理,同时阐述了电网连续运行数据样本生成,知识图谱分级共享,模块化仿真器构建等关键技术,并对该框架在调控业务场景中的应用进行了分析。该框架具备人工智能资源的全过程管理能力,能够为人工智能技术在调控领域中的深化应用提供有力的技术支撑。
In recent years
the application of artificial intelligence technology in the field of power grid dispatch and control has continued to develop
and with the construction of dispatching and control cloud
related businesses have put forward a strong demand for the sharing of artificial intelligence resources on the two-level architecture. This paper proposes a multi-level sharing framework for artificial intelligence resources based on dispatching and control cloud architecture
which realizes the full-process management of integrating
storing
and calling artificial intelligence resources such as data sample set
algorithm model
and reinforcement learning environment simulator on the multi-level dispatching and control cloud. Meanwhile
it elaborates on key technologies such as the generation of continuous operation data sample for power grid
hierarchical sharing of knowledge graph
and modularized simulator construction
and analyzes the application of this framework in the field of dispatching and control business scenarios. This framework has the ability to manage the entire process of integrating
storing
and calling artificial intelligence resources. This framework possesses the capability of full-process management of artificial intelligence resources
and can provides strong technical support for the deepening application of artificial intelligence technology in the field of power grid dispatching and control.
辛保安 . 新型电力系统构建方法论研究 [J]. 新型电力系统 , 2023 , 1 ( 1 ): 1 - 18 .
XIN Baoan . Research on the methodology of constructing new power systems [J]. New Type Power Systems , 2023 , 1 ( 1 ): 1 - 18 .
蒲天骄 , 韩 笑 . 新型电力系统中人工智能应用的关键技术 [J]. 电力信息与通信技术 , 2024 , 22 ( 1 ): 1 - 13 .
PU Tianjiao , HAN Xiao . Research on key technologies in the application of artificial intelligence in new type power systems [J]. Electric Power Information and Communication Technology , 2024 , 22 ( 1 ): 1 - 13 .
范士雄 , 刘幸蔚 , 於益军 , 等 . 基于多源数据和模型融合的超短期母线负荷预测方法 [J]. 电网技术 , 2021 , 45 ( 1 ): 243 - 250 .
FAN Shixiong , LIU Xingwei , YU Yijun , et al . Multi-source data and hybrid neural network based ultra-short-term bus load forecasting [J]. Power System Technology , 2021 , 45 ( 1 ): 243 - 250 .
孙铁军 , 曲丽萍 , 关海爽 , 等 . 基于智能优化的模糊Petri网的电网故障诊断 [J]. 控制工程 , 2021 , 28 ( 1 ): 127 - 134 .
SUN Tiejun , QU Liping , GUAN Haishuang , et al . Fault diagnosis of power grid based on intelligent optimization fuzzy petri net [J]. Control Engineering of China , 2021 , 28 ( 1 ): 127 - 134 .
汤 奕 , 崔 晗 , 李 峰 , 等 . 人工智能在电力系统暂态问题中的应用综述 [J]. 中国电机工程学报 , 2019 , 39 ( 1 ): 2 - 13 .
TANG Yi , CUI Han , LI Feng , et al . Review on artificial intelligence in power system transient stability analysis [J]. Proceedings of the CSEE , 2019 , 39 ( 1 ): 2 - 13 .
王之伟 , 陆 晓 , 刁瑞盛 , 等 . 基于深度强化学习的电网自主控制与决策技术 [J]. 电力工程技术 , 2020 , 39 ( 6 ): 34 - 43 .
WANG Zhiwei , LU Xiao , DIAO Ruisheng , et al . Deep-reinforcement-learning based autonomous control and decision making for power systems [J]. Electric Power Engineering Technology , 2020 , 39 ( 6 ): 34 - 43 .
陶洪铸 , 翟明玉 , 许洪强 , 等 . 适应调控领域应用场景的人工智能平台体系架构及关键技术 [J]. 电网技术 , 2020 , 44 ( 2 ): 412 - 419 .
TAO Hongzhu , ZHAI Mingyu , XU Hongqiang , et al . Architecture and key technologies of artificial intelligence platform oriented for power grid dispatching and control application scenarios [J]. Power System Technology , 2020 , 44 ( 2 ): 412 - 419 .
李 刚 , 李银强 , 王洪涛 , 等 . 电力设备健康管理知识图谱:基本概念、关键技术及研究进展 [J]. 电力系统自动化 , 2022 , 46 ( 3 ): 1 - 13 .
LI Gang , LI Yinqiang , WANG Hongtao , et al . Knowledge graph of power equipment health management:basic concepts,key technologies and research progress [J]. Automation of Electric Power Systems , 2022 , 46 ( 3 ): 1 - 13 .
孙 伟 , 朱世睿 , 杨建平 , 等 . 基于图卷积网络的微电网拓扑辨识 [J]. 电力系统自动化 , 2022 , 46 ( 5 ): 71 - 81 .
SUN Wei , ZHU Shirui , YANG Jianping , et al . Topology identification of microgrid based on graph convolutional network [J]. Automation of Electric Power Systems , 2022 , 46 ( 5 ): 71 - 81 .
王 波 , 王红霞 , 姚良忠 , 等 . 电力系统多模态数据融合模式及关键技术问题 [J]. 电力系统自动化 , 2022 , 46 ( 19 ): 188 - 199 .
WANG Bo , WANG Hongxia , YAO Liangzhong , et al . Multi-modal data fusion mode for power system and its key technical issues [J]. Automation of Electric Power Systems , 2022 , 46 ( 19 ): 188 - 199 .
XUE B C , ZOU L . Knowledge graph quality management:a comprehensive survey [J]. IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 5 ): 4969 - 4988 .
QIN B W , HUI B Y , WANG L H , et al . A survey on text-to-SQL parsing:concepts,methods,and future directions [J]. arXiv preprint , arXiv:2208. 13629 , 2022 .
LI B H , HOU Y T , CHE W X . Data augmentation approaches in natural language processing:a survey [J]. AI Open , 2022 , 3 : 71 - 90 .
SUTSKEVER I , VINYALS O , LE Q V . Sequence to sequence learning with neural networks [C]// Proceedings of the 27th International Conference on Neural Information Processing Systems . Montreal,Canada : MIT Press , 2014 : 3104 - 3112 .
WANG D Z R , DOU L X , CHE W X , et al . CONDA:state-based data augmentation for context-dependent text-to-SQL [J]. International Journal of Machine Learning and Cybernetics , 2024 , 15 ( 8 ): 3157 - 3168 .
0
浏览量
0
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621