
浏览全部资源
扫码关注微信
1.南方电网数字电网研究院有限公司,广东省广州市 510663
2.浙江大学南方电网人工智能创新联合研究中心,浙江省杭州市 310058
3.浙江大学计算机科学与技术学院,浙江省杭州市 310058
李鹏(1973—),男,博士,教授级高级工程师,主要研究方向:电网数字化、智能化。E-mail:lipeng@csg.cn
黄文琦(1988—),女,博士,高级工程师,主要研究方向:电力人工智能。E-mail:huangwqcsg@163.com
王鑫(1984—),男,通信作者,博士,副教授,硕士生导师,主要研究方向:机器学习、大数据分析、联邦学习等人工智能技术。E-mail:wangxin2009@zju.edu.cn
收稿:2022-10-30,
修回:2023-03-14,
网络出版:2023-12-06,
纸质出版:2024-01-10
移动端阅览
李鹏,黄文琦,王鑫等.数据与知识联合驱动的人工智能方法在电力调度中的应用综述[J].电力系统自动化,2024,48(1):160-175.
LI Peng,HUANG Wenqi,WANG Xin,et al.Review on Application of Combined Data-knowledge-driven Artificial Intelligence Methods in Power Dispatching[J].Automation of Electric Power Systems,2024,48(1):160-175.
李鹏,黄文琦,王鑫等.数据与知识联合驱动的人工智能方法在电力调度中的应用综述[J].电力系统自动化,2024,48(1):160-175. DOI: 10.7500/AEPS20221030007.
LI Peng,HUANG Wenqi,WANG Xin,et al.Review on Application of Combined Data-knowledge-driven Artificial Intelligence Methods in Power Dispatching[J].Automation of Electric Power Systems,2024,48(1):160-175. DOI: 10.7500/AEPS20221030007.
数据与知识联合驱动的人工智能方法是利用数据驱动和知识驱动各自优势、取长补短形成的一种更优的联合方法。文中对联合驱动方法的特点和现状进行了分析,并对其电力调度重点应用场景进行了分类统计。总结了各类场景中联合方法的创新性、相较于单一方法的提升以及应用优势。归纳出了电力调度数据与知识联合驱动方法的3种联合建模方式:数据驱动优化知识驱动、知识驱动辅助数据驱动、数据驱动与知识驱动混合模式。最后,从准确性和高效性的平衡、有效性、可靠性等5个维度讨论了联合建模时存在的挑战性问题,对联合驱动方法在电力调度中的适用场景进行了总结及展望。
The combined data-knowledge-driven artificial intelligence method is a better combined method that utilizes the advantages of data-driven and knowledge-driven methods
complementing each other’s strengths and weaknesses. The paper analyzes the characteristics and current situation of the combined-driven methods
classifies and calculates its key application scenarios in power dispatching. The innovation
improvement
and application advantages of combined-driven methods in various scenarios compared to single methods are summarized. Three combined modeling methods for power dispatching combined data-knowledge-driven methods have been summarized: data-driven optimizing knowledge-driven mode
knowledge-driven assisting data-riven mode
and hybrid data-knowledge-driven mode. Finally
the challenging issues in combined modeling are discussed from five dimensions: the balance between accuracy and efficiency
effectiveness
and reliability. The application scenarios of combined-driven methods in power dispatching are discussed and prospected.
戴彦 , 王刘旺 , 李媛 , 等 . 新一代人工智能在智能电网中的应用研究综述 [J]. 电力建设 , 2018 , 39 ( 10 ): 1 - 11 .
DAI Yan , WANG Liuwang , LI Yuan , et al . A brief survey on applications of new generation artificial intelligence in smart grids [J]. Electric Power Construction , 2018 , 39 ( 10 ): 1 - 11 .
国务院关于印发新一代人工智能发展规划的通知 [EB/OL].( 2017-07-20 )[ 2023-02-01 ]. https://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm https://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm .
Notice of the State Council on printing and distributing the development plan of a new generation of artificial intelligence [EB/OL]. ( 2017-07-20 )[ 2023-02-01 ]. https://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm https://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm .
中国南方电网有限责任公司 . 数字电网白皮书 [R]. 2020 .
China Southern Power Grid Company Limited . Digital power grid white paper [R]. 2020 .
李鹏 , 习伟 , 蔡田田 , 等 . 数字电网的理念、架构与关键技术 [J]. 中国电机工程学报 , 2022 , 42 ( 14 ): 5002 - 5017 .
LI Peng , XI Wei , CAI Tiantian , et al . Concept, architecture and key technologies of digital power grids [J]. Proceedings of the CSEE , 2022 , 42 ( 14 ): 5002 - 5017 .
中国人工智能 2 . 0发展战略研究项目组.中国人工智能2.0发展战略研究 [M]. 杭州 : 浙江大学出版社 ,2019.
China Artificial Intelligence 2 . 0 Development Strategy Research Project Team. China artificial intelligence 2.0 development strategy research [M]. Hangzhou : Zhejiang University Press , 2019.
潘云鹤 . 人工智能走向 2 .0[J]. Engineering ,2016, 2 ( 4 ): 51 - 61 .
PAN Yunhe . Heading toward artificial intelligence 2.0 [J]. Engineering , 2016 , 2 ( 4 ): 51 - 61 .
HUANG T , WANG J . A practical method of transient stability analysis of stochastic power systems based on EEAC [J]. International Journal of Electrical Power & Energy Systems , 2019 , 107 : 167 - 176 .
黄天罡 , 薛禹胜 , 林振智 , 等 . 动态EEAC的自适应分段映射 [J]. 电力系统自动化 , 2018 , 42 ( 21 ): 21 - 27 .
HUANG Tiangang , XUE Yusheng , LIN Zhenzhi , et al . Dynamic EEAC with adaptive subsection mapping [J]. Automation of Electric Power Systems , 2018 , 42 ( 21 ): 21 - 27 .
WANG Q , LI F , TANG Y , et al . Integrating model-driven and data-driven methods for power system frequency stability assessment and control [J]. IEEE Transactions on Power Systems , 2019 , 34 ( 6 ): 4557 - 4568 .
田芳 , 周孝信 , 于之虹 . 基于灵敏度分析和时域仿真的暂态稳定预防控制优化方法 [J]. 电力自动化设备 , 2018 , 38 ( 7 ): 155 - 161 .
TIAN Fang , ZHOU Xiaoxin , YU Zhihong . Optimization method of transient stability preventive control based on sensitivity analysis and time domain simulation [J]. Electric Power Automation Equipment , 2018 , 38 ( 7 ): 155 - 161 .
蒲志强 , 易建强 , 刘振 , 等 . 知识和数据协同驱动的群体智能决策方法研究综述 [J]. 自动化学报 , 2022 , 48 ( 3 ): 627 - 643 .
PU Zhiqiang , YI Jianqiang , LIU Zhen , et al . Knowledge-based and data-driven integrating methodologies for collective intelligence decision making: a survey [J]. Acta Automatica Sinica , 2022 , 48 ( 3 ): 627 - 643 .
李晗 , 萧德云 . 基于数据驱动的故障诊断方法综述 [J]. 控制与决策 , 2011 , 26 ( 1 ): 1 - 9 .
LI Han , XIAO Deyun . Survey on data driven fault diagnosis methods [J]. Control and Decision , 2011 , 26 ( 1 ): 1 - 9 .
南东亮 , 冯长有 , 曹晖 , 等 . 基于数据驱动的配电网停电预测模型 [J]. 电工电能新技术 , 2021 , 40 ( 12 ): 56 - 63 .
NAN Dongliang , FENG Changyou , CAO Hui , et al . Data-driven predictive model of distribution system blackout [J]. Advanced Technology of Electrical Engineering and Energy , 2021 , 40 ( 12 ): 56 - 63 .
XU J X , HOU Z S . Notes on data-driven system approaches [J]. Acta Automatica Sinica , 2009 , 35 ( 6 ): 668 - 675 .
郭鸿业 , 郑可迪 , 唐庆虎 , 等 . 数据驱动的电力市场研究:挑战与展望 [J]. 电力系统自动化 , 2023 , 47 ( 1 ): 200 - 215 .
GUO Hongye , ZHENG Kedi , TANG Qinghu , et al . Research on data-driven electricity market: challenges and prospects [J]. Automation of Electric Power Systems , 2023 , 47 ( 1 ): 200 - 215 .
姚建国 , 余涛 , 杨胜春 , 等 . 提升电网调度中人工智能可用性的混合增强智能知识演化技术 [J]. 电力系统自动化 , 2022 , 46 ( 20 ): 1 - 12 .
YAO Jianguo , YU Tao , YANG Shengchun , et al . Knowledge evolution technology based on hybrid-augmented intelligence for improving practicability of artificial intelligence in power grid dispatch [J]. Automation of Electric Power Systems , 2022 , 46 ( 20 ): 1 - 12 .
薛禹胜 , 赖业宁 . 大能源思维与大数据思维的融合(二):应用及探索 [J]. 电力系统自动化 , 2016 , 40 ( 8 ): 1 - 13 .
XUE Yusheng , LAI Yening . Integration of macro energy thinking and big data thinking: Part two applications and explorations [J]. Automation of Electric Power Systems , 2016 , 40 ( 8 ): 1 - 13 .
胡健雄 , 汤奕 , 李峰 , 等 . 电力系统中数据-物理融合模型的并联模式性能分析 [J]. 电力系统自动化 , 2022 , 46 ( 1 ): 15 - 24 .
HU Jianxiong , TANG Yi , LI Feng , et al . Performance analysis on parallel mode of data-physical fusion model in power system [J]. Automation of Electric Power Systems , 2022 , 46 ( 1 ): 15 - 24 .
SCHÖLKOPF B . Causality for machine learning [M]. New York, USA : Probabilistic and Causal Inference, ACM , 2022 : 765 - 804 .
CUI P , ATHEY S . Stable learning establishes some common ground between causal inference and machine learning [J]. Nature Machine Intelligence , 2022 , 4 ( 2 ): 110 - 115 .
ZHU Y H , ZABARAS N , KOUTSOURELAKIS P S , et al . Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data [J]. Journal of Computational Physics , 2019 , 394 : 56 - 81 .
KARNIADAKIS G E , KEVREKIDIS I G , LU L , et al . Physics-informed machine learning [J]. Nature Reviews Physics , 2021 , 3 ( 6 ): 422 - 440 .
CHEN Y T , HUANG D , ZHANG D X , et al . Theory-guided hard constraint projection(HCP): a knowledge-based data-driven scientific machine learning method [J]. Journal of Computational Physics , 2021 , 445 : 110624 .
CHEN Y , ZHANG D . Integration of knowledge and data in machine learning [J/OL]. [ 2022-10-30 ]. https://arxiv.org/abs/2202.10337v1 https://arxiv.org/abs/2202.10337v1 .
王彪 , 吕洋 , 陈中 , 等 . 考虑信息时移的分布式光伏机理-数据混合驱动短期功率预测 [J]. 电力系统自动化 , 2022 , 46 ( 11 ): 67 - 74 .
WANG Biao , LYU Yang , CHEN Zhong , et al . Hybrid mechanism-data-driven short-term power forecasting of distributed photovoltaic considering information time shift [J]. Automation of Electric Power Systems , 2022 , 46 ( 11 ): 67 - 74 .
江雪晨 , 王大志 , 宁一 , 等 . 电网故障最优诊断的查询方法 [J]. 高电压技术 , 2017 , 43 ( 4 ): 1311 - 1316 .
JIANG Xuechen , WANG Dazhi , NING Yi , et al . Query method for optimal diagnosis of power system faults [J]. High Voltage Engineering , 2017 , 43 ( 4 ): 1311 - 1316 .
王志杨 , 张靖 , 何宇 , 等 . 数据与模型混合驱动的区域综合能源系统双层优化调度决策方法 [J]. 电网技术 , 2022 , 46 ( 10 ): 3797 - 3813 .
WANG Zhiyang , ZHANG Jing , HE Yu , et al . Double-level optimal scheduling decision method for regional comprehensive energy system driven by data and model [J]. Power System Technology , 2022 , 46 ( 10 ): 3797 - 3813 .
邓柏荣 , 陈俊斌 , 丁巧宜 , 等 . 融合电网运行场景聚类的多任务深度强化学习优化调度 [J]. 电网技术 , 2023 , 47 ( 3 ): 978 - 987 .
DENG Bairong , CHEN Junbin , DING Qiaoyi , et al . Multi task deep reinforcement learning optimal dispatching based on grid operation scenario clustering [J]. Power System Technology , 2023 , 47 ( 3 ): 978 - 987 .
LI P , GUAN X H , WU J , et al . Modeling dynamic spatial correlations of geographically distributed wind farms and constructing ellipsoidal uncertainty sets for optimization-based generation scheduling [J]. IEEE Transactions on Sustainable Energy , 2015 , 6 ( 4 ): 1594 - 1605 .
刘宏伟 , 赵丽萍 , 李玉付 . 信息-物理-社会视角下的配电系统优化调度 [J]. 南方电网技术 , 2022 , 16 ( 5 ): 26 - 33 .
LIU Hongwei , ZHAO Liping , LI Yufu . Optimal dispatch of distribution system under cyber-physical-social perspective [J]. Southern Power System Technology , 2022 , 16 ( 5 ): 26 - 33 .
杨少波 , 刘道伟 , 安军 , 等 . 基于长短期记忆网络的电网动态轨迹趋势预测方法 [J]. 中国电机工程学报 , 2020 , 40 ( 9 ): 2854 - 2866 .
YANG Shaobo , LIU Daowei , AN Jun , et al . Trend prediction method of power network dynamic trajectory based on long short term memory neural networks [J]. Proceedings of the CSEE , 2020 , 40 ( 9 ): 2854 - 2866 .
吕睿 , 薛禹胜 , 黄天罡 . 准定量法与精确定量法在暂态稳定分析中的融合 [J]. 电力系统自动化 , 2021 , 45 ( 4 ): 12 - 19 .
LYU Rui , XUE Yusheng , HUANG Tiangang . Integration of quasi-quantitative and quantitative methods in transient stability analysis [J]. Automation of Electric Power Systems , 2021 , 45 ( 4 ): 12 - 19 .
李亚鹏 , 韩旭 , 于旭光 , 等 . 模型和数据混合驱动的双边协商电力市场合约价格预测方法 [J]. 电力系统自动化 , 2022 , 46 ( 18 ): 179 - 189 .
LI Yapeng , HAN Xu , YU Xuguang , et al . Model and data hybrid-driven contract price forecasting method for bilateral negotiation power market [J]. Automation of Electric Power Systems , 2022 , 46 ( 18 ): 179 - 189 .
尚宇炜 , 马钊 , 彭晨阳 , 等 . 内嵌专业知识和经验的机器学习方法探索(一):引导学习的提出与理论基础 [J]. 中国电机工程学报 , 2017 , 37 ( 19 ): 5560 - 5571 .
SHANG Yuwei , MA Zhao , PENG Chenyang , et al . Study of a novel machine learning method embedding expertise: Part Ⅰ proposals and fundamentals of guiding learning [J]. Proceedings of the CSEE , 2017 , 37 ( 19 ): 5560 - 5571 .
尚宇炜 , 郭剑波 , 吴文传 , 等 . 电力脑初探:一种多模态自适应学习系统 [J]. 中国电机工程学报 , 2018 , 38 ( 11 ): 3133 - 3143 .
SHANG Yuwei , GUO Jianbo , WU Wenchuan , et al . Preliminary study of electric power brain: a multimodal adaptive learning system [J]. Proceedings of the CSEE , 2018 , 38 ( 11 ): 3133 - 3143 .
李峰 , 王琦 , 胡健雄 , 等 . 数据与知识联合驱动方法研究进展及其在电力系统中应用展望 [J]. 中国电机工程学报 , 2021 , 41 ( 13 ): 4377 - 4390 .
LI Feng , WANG Qi , HU Jianxiong , et al . Combined data-driven and knowledge-driven methodology research advances and its applied prospect in power systems [J]. Proceedings of the CSEE , 2021 , 41 ( 13 ): 4377 - 4390 .
王继业 . “数字国网”战略及人工智能应用 [R]. 北京 : 中国电力科学研究院 , 2018 .
WANG Jiye . “ Digital state grid” strategy and application of artificial intelligence [R]. Beijing : China Electric Power Research Institute , 2018 .
贺兴 , 艾芊 , 朱天怡 , 等 . 数字孪生在电力系统应用中的机遇和挑战 [J]. 电网技术 , 2020 , 44 ( 6 ): 2009 - 2019 .
HE Xing , AI Qian , ZHU Tianyi , et al . Opportunities and challenges of the digital twin in power system applications [J]. Power System Technology , 2020 , 44 ( 6 ): 2009 - 2019 .
蒲天骄 , 陈盛 , 赵琦 , 等 . 能源互联网数字孪生系统框架设计及应用展望 [J]. 中国电机工程学报 , 2021 , 41 ( 6 ): 2012 - 2029 .
PU Tianjiao , CHEN Sheng , ZHAO Qi , et al . Framework design and application prospect for digital twins system of energy Internet [J]. Proceedings of the CSEE , 2021 , 41 ( 6 ): 2012 - 2029 .
相晨萌 , 曾四鸣 , 闫鹏 , 等 . 数字孪生技术在电网运行中的典型应用与展望 [J]. 高电压技术 , 2021 , 47 ( 5 ): 1564 - 1575 .
XIANG Chenmeng , ZENG Siming , YAN Peng , et al . Typical application and prospect of digital twin technology in power grid operation [J]. High Voltage Engineering , 2021 , 47 ( 5 ): 1564 - 1575 .
王鑫 , 王霖 , 余芸 , 等 . 数字孪生电网的特性、架构及应用综述 [J]. 电子与信息学报 , 2022 , 44 ( 11 ): 3721 - 3733 .
WANG Xin , WANG Lin , YU Yun , et al . A summary of the characteristics, architecture and application of digital twin power grid [J]. Journal of Electronics & Information Technology , 2022 , 44 ( 11 ): 3721 - 3733 .
徐彪 , 尹项根 , 张哲 , 等 . 矩阵算法和优化算法相结合的配电网故障定位 [J]. 电力系统自动化 , 2019 , 43 ( 5 ): 152 - 158 .
XU Biao , YIN Xianggen , ZHANG Zhe , et al . Fault location for distribution network based on matrix algorithm and optimization algorithm [J]. Automation of Electric Power Systems , 2019 , 43 ( 5 ): 152 - 158 .
JUNG D , NG K Y , FRISK E , et al . Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation [J]. Control Engineering Practice , 2018 , 80 : 146 - 156 .
TZANIS N , ANDRIOPOULOS N , MAGKLARAS A , et al . A hybrid cyber physical digital twin approach for smart grid fault prediction [C]// 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) , June 10-12 , 2020 , Tampere, Finland : 393 - 397 .
HUANG C C , QI W C , WANG Q Q , et al . Subsequent commutation failure prediction of HVDC by integrating physical-driven and model-driven methods [C]// 2020 4th International Conference on HVDC , November 6-9 , 2020 , Xi’an, China : 242 - 247 .
鲍颜红 , 冯长有 , 任先成 , 等 . 基于支持向量机的在线暂态稳定故障筛选 [J]. 电力系统自动化 , 2019 , 43 ( 22 ): 52 - 58 .
BAO Yanhong , FENG Changyou , REN Xiancheng , et al . Online transient stability fault screening based on support vector machine [J]. Automation of Electric Power Systems , 2019 , 43 ( 22 ): 52 - 58 .
黄登一 , 刘灏 , 毕天姝 , 等 . 基于模型-数据联合驱动的电网扰动定位方法 [J]. 电网技术 , 2023 , 47 ( 3 ): 1206 - 1217 .
HUANG Dengyi , LIU Hao , BI Tianshu , et al . A joint model and data-driven based power system disturbance localization approach [J]. Power System Technology , 2023 , 47 ( 3 ): 1206 - 1217 .
张淑清 , 李君 , 姜安琦 , 等 . 基于FPA-VMD和BiLSTM神经网络的新型两阶段短期电力负荷预测 [J]. 电网技术 , 2022 , 46 ( 8 ): 3269 - 3279 .
ZHANG Shuqing , LI Jun , JIANG Anqi , et al . A novel two-stage model based on FPA-VMD and BiLSTM neural network for short-term power load forecasting [J]. Power System Technology , 2022 , 46 ( 8 ): 3269 - 3279 .
CHEN Y T , ZHANG D X . Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory [J]. Advances in Applied Energy , 2021 , 1 : 100004 .
王琦 , 李峰 , 汤奕 , 等 . 基于物理—数据融合模型的电网暂态频率特征在线预测方法 [J]. 电力系统自动化 , 2018 , 42 ( 19 ): 1 - 9 .
WANG Qi , LI Feng , TANG Yi , et al . On-line prediction method of transient frequency characteristics for power grid based on physical-statistical model [J]. Automation of Electric Power Systems , 2018 , 42 ( 19 ): 1 - 9 .
WANG X X , BA Y , LIU R , et al . Model-data integration driven based power system frequency response model [C]// 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) , June 28-30 , 2021 , Dalian, China : 107 - 112 .
温玉琦 . 一种基于数据驱动与物理模型融合的含风电系统频率响应分析方法 [J]. 广东电力 , 2019 , 32 ( 9 ): 167 - 176 .
WEN Yuqi . A frequency response analysis method for wind power system based on data driven-physical model fusion [J]. Guangdong Electric Power , 2019 , 32 ( 9 ): 167 - 176 .
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 .
李可军 , 亓孝武 , 魏本刚 , 等 . 基于核极限学习机误差预测修正的变压器顶层油温预测 [J]. 高电压技术 , 2017 , 43 ( 12 ): 4045 - 4053 .
LI Kejun , QI Xiaowu , WEI Bengang , et al . Prediction of transformer top oil temperature based on kernel extreme learning machine error prediction and correction [J]. High Voltage Engineering , 2017 , 43 ( 12 ): 4045 - 4053 .
刘礼恺 , 胡泽春 , 罗浩成 , 等 . 基于数据驱动和条件概率的调频容量需求计算方法 [J]. 电网技术 , 2021 , 45 ( 7 ): 2569 - 2576 .
LIU Likai , HU Zechun , LUO Haocheng , et al . Data-driven based regulation reserve capacity requirement calculation using conditional probability [J]. Power System Technology , 2021 , 45 ( 7 ): 2569 - 2576 .
程浩忠 , 胡枭 , 王莉 , 等 . 区域综合能源系统规划研究综述 [J]. 电力系统自动化 , 2019 , 43 ( 7 ): 2 - 13 .
CHENG Haozhong , HU Xiao , WANG Li , et al . Review on research of regional integrated energy system planning [J]. Automation of Electric Power Systems , 2019 , 43 ( 7 ): 2 - 13 .
张璐 , 黄睿 , 王照琪 , 等 . 考虑恢复力与经济性均衡的配电网移动储能优化配置策略 [J]. 电力系统自动化 , 2020 , 44 ( 21 ): 23 - 31 .
ZHANG Lu , HUANG Rui , WANG Zhaoqi , et al . Optimal configuration strategy of mobile energy storage in distribution network considering balance between resilience and economy [J]. Automation of Electric Power Systems , 2020 , 44 ( 21 ): 23 - 31 .
TAN Y , CHEN Y Y , LI Y , et al . Linearizing power flow model: a hybrid physical model-driven and data-driven approach [J]. IEEE Transactions on Power Systems , 2020 , 35 ( 3 ): 2475 - 2478 .
杨晓东 , 严剑峰 , 刘佳霖 . 结合深度强化学习与人工经验的电网输电断面功率调整方法 [J]. 电力系统自动化 , 2023 , 47 ( 15 ): 133 - 141 .
YANG Xiaodong , YAN Jianfeng , LIU Jialin . Power adjustment method for transmission section in power grid combining deep reinforcement learning and artificial experience [J]. Automation of Electric Power Systems , 2023 , 47 ( 15 ): 133 - 141 .
卢锦玲 , 颜禄涵 , 腊志源 , 等 . 基于数字孪生与动态能效模型的综合能源系统实时优化调度策略 [J]. 电网技术 , 2023 , 47 ( 1 ): 226 - 238 .
LU Jinling , YAN Luhan , LA Zhiyuan , et al . Real-time optimal scheduling strategy of integrated energy system based on digital twinning and dynamic energy efficiency model [J]. Power System Technology , 2023 , 47 ( 1 ): 226 - 238 .
LI F , WANG Q , TANG Y , et al . Hybrid analytical and data-driven modeling based instance-transfer method for power system online transient stability assessment [J]. CSEE Journal of Power and Energy Systems , 2021 , PP( 99 ): 1 - 10 .
薛禹胜 , 黄天罡 , 陈国平 , 等 . 关于暂态稳定分析算例筛选的评述 [J]. 电力系统自动化 , 2019 , 43 ( 6 ): 1 - 14 .
XUE Yusheng , HUANG Tiangang , CHEN Guoping , et al . Review on case filtering in transient stability analysis [J]. Automation of Electric Power Systems , 2019 , 43 ( 6 ): 1 - 14 .
胡伟 , 郑乐 , 闵勇 , 等 . 基于深度学习的电力系统故障后暂态稳定评估研究 [J]. 电网技术 , 2017 , 41 ( 10 ): 3140 - 3146 .
HU Wei , ZHENG Le , MIN Yong , et al . Research on power system transient stability assessment based on deep learning of big data technique [J]. Power System Technology , 2017 , 41 ( 10 ): 3140 - 3146 .
王亮 , 韩冬 , 王长江 , 等 . 基于MVEE和LSPTSVM的电力系统暂态稳定评估 [J]. 电力系统保护与控制 , 2020 , 48 ( 17 ): 45 - 54 .
WANG Liang , HAN Dong , WANG Changjiang , et al . Power system transient stability assessment based on MVEE and LSPTSVM [J]. Power System Protection and Control , 2020 , 48 ( 17 ): 45 - 54 .
李永康 , 刘宝柱 , 胡俊杰 . 基于数据驱动与时域仿真融合的电力系统暂态稳定快速评估 [J]. 电网技术: 2023 , 47 ( 11 ): 4386 - 4395 .
LI Yongkang , LIU Baozhu , HU Junjie . Rapid evaluation of power system transient stability based on fusion of data-driven and time-domain simulation [J]. Power System Technology 2023 , 47 ( 11 ): 4386 - 4395 .
黄天恩 , 郭庆来 , 孙宏斌 , 等 . 模型-数据混合驱动的电网安全特征选择和知识发现关键技术与工程应用 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 95 - 101 .
HUANG Tianen , GUO Qinglai , SUN Hongbin , et al . Hybrid model and data driven concepts for power system security feature selection and knowledge discovery: key technologies and engineering application [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 95 - 101 .
杨斌 , 杜文娟 , 王海风 . 数据驱动下的虚拟同步发电机等效建模 [J]. 电网技术 , 2020 , 44 ( 1 ): 35 - 43 .
YANG Bin , DU Wenjuan , WANG Haifeng . Equivalent modeling of virtual synchronous generator based on data-driven method [J]. Power System Technology , 2020 , 44 ( 1 ): 35 - 43 .
BELLIZIO F , CREMER J L , SUN M Y , et al . A causality based feature selection approach for data-driven dynamic security assessment [J]. Electric Power Systems Research , 2021 , 201 : 107537 .
LI F , WANG Q , TANG Y , et al . An integrated method for critical clearing time prediction based on a model-driven and ensemble cost-sensitive data-driven scheme [J]. International Journal of Electrical Power & Energy Systems , 2021 , 125 : 106513 .
吴滨源 , 李建文 , 李永刚 , 等 . 基于知识和数据联合驱动的并网逆变器多工况阻抗获取方法 [J]. 电力系统自动化 , 2022 , 46 ( 6 ): 83 - 91 .
WU Binyuan , LI Jianwen , LI Yonggang , et al . Combined data-knowledge-driven impedance acquisition method for grid-connected inverter under multiple operation conditions [J]. Automation of Electric Power Systems , 2022 , 46 ( 6 ): 83 - 91 .
强子玥 , 吴俊勇 , 李宝琴 , 等 . 基于改进AlexNet的电力系统暂态功角失稳紧急控制策略 [J]. 高电压技术 , 2022 , 48 ( 7 ): 2794 - 2804 .
QIANG Ziyue , WU Junyong , LI Baoqin , et al . Emergency control strategy for transient angle instability of power system based on improved AlexNet [J]. High Voltage Engineering , 2022 , 48 ( 7 ): 2794 - 2804 .
庄颖睿 , 肖谭南 , 程林 , 等 . 基于时空图卷积网络的电力系统暂态稳定评估 [J]. 电力系统自动化 , 2022 , 46 ( 11 ): 11 - 18 .
ZHUANG Yingrui , XIAO Tannan , CHENG Lin , et al . Transient stability assessment of power system based on spatio-temporal graph convolutional network [J]. Automation of Electric Power Systems , 2022 , 46 ( 11 ): 11 - 18 .
李柏堉 , 赵津蔓 , 韩肖清 , 等 . 基于双智能体深度强化学习的电力系统静态安全预防控制方法 [J]. 中国电机工程学报 , 2023 , 43 ( 5 ): 1818 - 1831 .
LI Baiyu , ZHAO Jinman , HAN Xiaoqing , et al . Static security oriented preventive control of power system based on double deep reinforcement learning [J]. Proceedings of the CSEE , 2023 , 43 ( 5 ): 1818 - 1831 .
傅一苇 , 陈磊 , 马子明 , 等 . 含数据驱动型稳定约束的电力系统预防控制 [J]. 中国电机工程学报 , 2022 , 42 ( 15 ): 5417 - 5430 .
FU Yiwei , CHEN Lei , MA Ziming , et al . Preventive control of power system with analytic data-driven stability constraints [J]. Proceedings of the CSEE , 2022 , 42 ( 15 ): 5417 - 5430 .
LIU J Z , ZHANG Y C , MENG K , et al . Real-time emergency load shedding for power system transient stability control: a risk-averse deep learning method [J]. Applied Energy , 2022 , 307 : 118221 .
唐成鹏 , 张粒子 , 刘方 , 等 . 基于多智能体强化学习的电力现货市场定价机制研究(一):不同定价机制下发电商报价双层优化模型 [J]. 中国电机工程学报 , 2021 , 41 ( 2 ): 536 - 553 .
TANG Chengpeng , ZHANG Lizi , LIU Fang , et al . Research on pricing mechanism of electricity spot market based on multi-agent reinforcement learning(part Ⅰ): Bi-level optimization model for generators under different pricing mechanisms [J]. Proceedings of the CSEE , 2021 , 41 ( 2 ): 536 - 553 .
RUAN G C , WANG J X , ZHONG H W , et al . Improving sample efficiency of deep learning models in electricity market [J]. IEEE Transactions on Power Systems , 2022 , PP( 99 ): 1 - 13 .
MA J , OPPONG A , ADJEI G K B , et al . Demand and supply-side determinants of electric power consumption and representative roadmaps to 100% renewable systems [J]. Journal of Cleaner Production , 2021 , 299 : 126832 .
BU F K , MA Z X , YUAN Y X , et al . WECC composite load model parameter identification using evolutionary deep reinforcement learning [J]. IEEE Transactions on Smart Grid , 2020 , 11 ( 6 ): 5407 - 5417 .
STRÖFER C M , WU J L , XIAO H , et al . Data-driven, physics-based feature extraction from fluid flow fields [EB/OL]. [ 2023-02-01 ]. https://arxiv.org/abs/1802.00775 https://arxiv.org/abs/1802.00775 .
尚宇炜 , 郭剑波 , 吴文传 , 等 . 数据-知识融合的机器学习(1):模型分析 [J]. 中国电机工程学报 , 2019 , 39 ( 15 ): 4406 - 4416 .
SHANG Yuwei , GUO Jianbo , WU Wenchuan , et al . Machine learning methods embedded with domain knowledge(part Ⅰ): model analysis [J]. Proceedings of the CSEE , 2019 , 39 ( 15 ): 4406 - 4416 .
王甜婧 , 汤涌 , 郭强 , 等 . 基于知识经验和深度强化学习的大电网潮流计算收敛自动调整方法 [J]. 中国电机工程学报 , 2020 , 40 ( 8 ): 2396 - 2406 .
WANG Tianjing , TANG Yong , GUO Qiang , et al . Automatic adjustment method of power flow calculation convergence for large-scale power grid based on knowledge experience and deep reinforcement learning [J]. Proceedings of the CSEE , 2020 , 40 ( 8 ): 2396 - 2406 .
HU X Y , HU H J , VERMA S , et al . Physics-guided deep neural networks for power flow analysis [J]. IEEE Transactions on Power Systems , 2021 , 36 ( 3 ): 2082 - 2092 .
尚宇炜 , 马钊 , 彭晨阳 , 等 . 内嵌专业知识和经验的机器学习方法探索(二):引导学习的应用与实践 [J]. 中国电机工程学报 , 2017 , 37 ( 20 ): 5852 - 5861 .
SHANG Yuwei , MA Zhao , PENG Chenyang , et al . Study of a novel machine learning method embedding expertise: part Ⅱ applications and practices of guiding learning [J]. Proceedings of the CSEE , 2017 , 37 ( 20 ): 5852 - 5861 .
李家宁 , 熊睿彬 , 兰艳艳 , 等 . 因果机器学习的前沿进展综述 [J]. 计算机研究与发展 , 2023 , 60 ( 1 ): 59 - 84 .
LI Jianing , XIONG Ruibin , LAN Yanyan , et al . A summary of the frontier progress of causal machine learning [J]. Journal of Computer Research and Development , 2023 , 60 ( 1 ): 59 - 84 .
王力成 , 邓宝华 , 黄刚 , 等 . 知识-数据混合驱动的电网频率协同控制算法 [J]. 中国电机工程学报 , 2022 , 42 ( 23 ): 8523 - 8534 .
WANG Licheng , DENG Baohua , HUANG Gang , et al . Knowledge-data hybrid driven frequency cooperative control algorithm for power grid [J]. Proceedings of the CSEE , 2022 , 42 ( 23 ): 8523 - 8534 .
陆俊 , 陈志敏 , 龚钢军 , 等 . 基于极限学习机的居民用电行为分类分析方法 [J]. 电力系统自动化 , 2019 , 43 ( 2 ): 97 - 104 .
LU Jun , CHEN Zhimin , GONG Gangjun , et al . Classification analysis method for electricity consumption behavior based on extreme learning machine algorithm [J]. Automation of Electric Power Systems , 2019 , 43 ( 2 ): 97 - 104 .
王凯亮 , 陆俊 , 徐志强 , 等 . 基于先验知识与DBM采样的类不平衡用电数据分类方法 [J]. 电力系统自动化 , 2019 , 43 ( 20 ): 57 - 64 .
WANG Kailiang , LU Jun , XU Zhiqiang , et al . Classification method of unbalanced power consumption data based on prior knowledge and deep Boltzmann machine sampling [J]. Automation of Electric Power Systems , 2019 , 43 ( 20 ): 57 - 64 .
朱继忠 , 骆腾燕 , 吴皖莉 , 等 . 综合能源系统运行可靠性评估评述Ⅱ:数据驱动法与模型-数据混合驱动法 [J]. 电工技术学报 , 2022 , 37 ( 13 ): 3227 - 3240 .
ZHU Jizhong , LUO Tengyan , WU Wanli , et al . A review of operational reliability assessment of integrated energy systems Ⅱ: data-driven method and model-data hybrid driven method [J]. Transactions of China Electrotechnical Society , 2022 , 37 ( 13 ): 3227 - 3240 .
蒲天骄 , 张中浩 , 谈元鹏 , 等 . 电力人工智能技术理论基础与发展展望(二):自主学习与应用初探 [J/OL]. 中国电机工程学报 : 1 - 14 [ 2022-10-02 ].html://kns.cnki.net/kcms/detail/ 11 .2107.TM. 20220729.1748 .004.html.
PU Tianjiao , ZHANG Zhonghao , TAN Yuanpeng , et al . Theoretical primer and directions of electric power artificial intelligence Ⅱ: self-directed learning and preliminary application [J/OL]. Proceedings of the CSEE : 1 - 14 [ 2022-10-02 ]. html://kns.cnki.net/kcms/detail/ 11 .2107.TM. 20220729.1748 .004.html.
0
浏览量
0
下载量
5
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621