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1.河海大学能源与电气学院,江苏省南京市 210098
2.国网江苏省电力有限公司,江苏省南京市 210024
3.北京科东电力控制系统有限责任公司,北京市 100085
Received:20 July 2020,
Revised:2020-09-21,
Published Online:07 November 2020,
Published:25 December 2020
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赵晋泉,夏雪,徐春雷等.新一代人工智能技术在电力系统调度运行中的应用评述[J].电力系统自动化,2020,44(24):1-10.
ZHAO Jinquan,XIA Xue,XU Chunlei,et al.Review on Application of New Generation Artificial Intelligence Technology in Power System Dispatching and Operation[J].Automation of Electric Power Systems,2020,44(24):1-10.
赵晋泉,夏雪,徐春雷等.新一代人工智能技术在电力系统调度运行中的应用评述[J].电力系统自动化,2020,44(24):1-10. DOI: 10.7500/AEPS20200720009.
ZHAO Jinquan,XIA Xue,XU Chunlei,et al.Review on Application of New Generation Artificial Intelligence Technology in Power System Dispatching and Operation[J].Automation of Electric Power Systems,2020,44(24):1-10. DOI: 10.7500/AEPS20200720009.
以深度学习、强化学习为代表的新一代人工智能技术及其应用是当前电力系统领域的研究热点。人工智能技术具有不依赖物理机理,计算速度快,辨别效率高等优点。但人工智能固有的可解释性差、稳定性弱等缺点也制约了其在电力系统一些场景的应用。文中梳理了新一代人工智能技术在电力系统负荷和新能源预测、故障诊断、在线稳定性评估、频率及电压优化控制和电网运行方式制定等调度运行场景中的应用,并进行了分析和评述。总结了现有研究中存在的问题,指出人工智能技术的应用应当以问题为导向,以场景为基础,以应用为目的。最后,对未来人工智能技术在电力系统调度运行中的应用作出了展望。
The new generation of artificial intelligence technology and its application represented by deep learning and reinforcement learning are the research hotspots in the field of power systems. Artificial intelligence technology has the advantages of independence of physical mechanism
high calculation speed and high discrimination efficiency. However
the inherent disadvantages of artificial intelligence
such as poor interpretability and weak stability
restrict its application in some scenarios of power systems. In this paper
the application of new generation artificial intelligence technology in power system load and renewable energy forecasting
fault diagnosis
on-line stability assessment
frequency and voltage optimal control and power grid operation mode formulation are summarized and analyzed. This paper summarizes the existing research problems and points out that the application of artificial intelligence technology should be problem-oriented
scenario-based and application-targeted. Finally
the future application of artificial intelligence technology in dispatching and operation of power system is prospected.
韩祯祥 , 文福拴 . 电力系统中专家系统的应用综述 [J]. 电力系统自动化 , 1993 , 17 ( 3 ): 51 - 56 .
HAN Zhenxiang , WEN Fushuan . The survey of the application of expert system to power system [J]. Automation of Electric Power Systems , 1993 , 17 ( 3 ): 51 - 56 .
中华人民共和国国务院 . 中国制造2025 [R].2015.
State Council of the PRC . Made in China 2025 [R]. 2015.
中华人民共和国国务院 . 新一代人工智能发展规划 [EB/OL].[ 2020-05-21 ]. http://www.gov.cn/zhengce/content/201707/20/content_5211996.htm http://www.gov.cn/zhengce/content/201707/20/content_5211996.htm .
State Council of the PRC . Development plan of new generation artificial intelligence [EB/OL]. [ 2020-05-21 ]. http://www.gov.cn/zhengce/content/201707/20/content_5211996.htm http://www.gov.cn/zhengce/content/201707/20/content_5211996.htm .
中国电子技术标准化研究院 . 人工智能标准化白皮书(2018版) [R].2018.
China Institute of Electronic Technology Standardization . The white paper of artificial intelligence standardization(2018 Edition) [R]. 2018.
杨挺 , 赵黎媛 , 王成山 . 人工智能在电力系统及综合能源系统中的应用综述 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 2 - 14 .
YANG Ting , ZHAO Liyuan , WANG Chengshan . Review on application of artificial intelligence in power system and integrated energy system [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 2 - 14 .
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 .
赵俊华 , 文福拴 , 薛禹胜 , 等 . 电力CPS的架构及其实现技术与挑战 [J]. 电力系统自动化 , 2010 , 34 ( 16 ): 1 - 7 .
ZHAO Junhua , WEN Fushuan , XUE Yusheng , et al . Cyber physical power systems: architecture, implementation techniques and challenges [J]. Automation of Electric Power Systems , 2010 , 34 ( 16 ): 1 - 7 .
HINTON G , SALAKHUTDINOV R . Reducing the dimensionality of data with neural networks [J]. Science , 2006 , 313 : 504 - 507 .
HINTON G , OSINDERO S , THE Y . A fast learning algorithm for deep belief nets [J]. Neural Computation , 2006 , 18 ( 7 ): 1527 - 1554 .
孙志军 , 薛磊 , 许阳明 , 等 . 深度学习研究综述 [J]. 计算机应用研究 , 2012 , 29 ( 8 ): 2806 - 2810 .
SUN Zhijun , XUE Lei , XU Yangming , et al . Overview of deep learning [J]. Application Research of Computers , 2012 , 29 ( 8 ): 2806 - 2810 .
ERHAN D , BENGIO Y , COURVILLE A , et al . Why does unsupervised pre-training help deep learning? [J]. Journal of Machine Learning Research , 2010 , 11 : 625 - 660 .
袁非牛 , 章琳 , 史劲亭 , 等 . 自编码神经网络理论及应用综述 [J]. 计算机学报 , 2019 , 42 ( 1 ): 203 - 230 .
YUAN Feiniu , ZHANG Lin , SHI Jinting , et al . Theories and application of auto-encoder neural network: a literature survey [J]. Chinese Journal of Computer , 2019 , 42 ( 1 ): 203 - 230 .
周飞燕 , 金林鹏 , 董军 . 卷积神经网络研究综述 [J]. 计算机学报 , 2017 , 40 ( 6 ): 1229 - 1251 .
ZHOU Feiyan , JIN Linpeng , DONG Jun . Review of convolution neural network [J]. Chinese Journal of Computer , 2017 , 40 ( 6 ): 1229 - 1251 .
刘全 , 翟建伟 , 章宗长 , 等 . 深度强化学习综述 [J]. 计算机学报 , 2018 , 41 ( 1 ): 1 - 27 .
LIU Quan , ZHAI Jianwei , ZHANG Zongchang , et al . A survey on deep reinforcement learning [J]. Chinese Journal of Computer , 2018 , 41 ( 1 ): 1 - 27 .
庄福振 , 罗平 , 何清 , 等 . 迁移学习研究进展 [J]. 软件学报 , 2015 , 26 ( 1 ): 26 - 39 .
ZHUANG Fuzhen , LUO Ping , HE Qing , et al . Survey on transfer learning research [J]. Journal of Software , 2015 , 26 ( 1 ): 26 - 39 .
庞昊 , 高金峰 , 杜耀恒 . 基于时间卷积网络分位数回归的短期负荷概率密度预测方法 [J]. 电网技术 , 2020 , 44 ( 4 ): 1343 - 1349 .
PANG Hao , GAO Jinfeng , DU Yaoheng . A short-term load probability density prediction based on quantile regression of time convolution network [J]. Power System Technology , 2020 , 44 ( 4 ): 1343 - 1349 .
赵兵 , 王增平 , 纪维佳 , 等 . 基于注意力机制的CNN-GRU短期电力负荷预测方法 [J]. 电网技术 , 2019 , 43 ( 12 ): 4370 - 4376 .
ZHAO Bing , WANG Zengping , JI Weijia , et al . A short-term power load forecasting method based on attention mechanism of CNN-GRU [J]. Power System Technology , 2019 , 43 ( 12 ): 4370 - 4376 .
梁智 , 孙国强 , 李虎成 , 等 . 基于VMD与PSO优化深度信念网络的短期负荷预测 [J]. 电网技术 , 2018 , 42 ( 2 ): 598 - 606 .
LIANG Zhi , SUN Guoqiang , LI Hucheng , et al . Short-term load forecasting based on VMD and PSO optimized deep belief network [J]. Power System Technology , 2018 , 42 ( 2 ): 598 - 606 .
孔祥玉 , 郑锋 , 鄂志君 , 等 . 基于深度信念网络的短期负荷预测方法 [J]. 电力系统自动化 , 2018 , 42 ( 5 ): 133 - 139 .
KONG Xiangyu , ZHENG Feng , E Zhijun , et al . Short-term load foresting based on deep belief network [J]. Automation of Electric Power Systems , 2018 , 42 ( 5 ): 133 - 139 .
张宇帆 , 艾芊 , 林琳 , 等 . 基于深度长短时记忆网络的区域级超短期负荷预测方法 [J]. 电网技术 , 2019 , 43 ( 6 ): 1884 - 1892 .
ZHANG Yufan , AI Qian , LIN Lin , et al . A very short-term load forecasting method based on deep LSTM RNN at zone level [J]. Power System Technology , 2019 , 43 ( 6 ): 1884 - 1892 .
陈振宇 , 刘金波 , 李晨 , 等 . 基于LSTM与XGBoost组合模型的超短期电力负荷预测 [J]. 电网技术 , 2020 , 44 ( 2 ): 614 - 620 .
CHEN Zhenyu , LIU Jinbo , LI Chen , et al . Ultra short-term power load forecasting based on combined LSTM-XGBoost model [J]. Power System Technology , 2020 , 44 ( 2 ): 614 - 620 .
吴润泽 , 包正睿 , 宋雪莹 , 等 . 基于深度学习的电网短期负荷预测方法研究 [J]. 现代电力 , 2018 , 35 ( 2 ): 43 - 48 .
WU Runze , BAO Zhengrui , SONG Xueying , et al . Research on short-term load forecasting method of power grid based on deep learning [J]. Modern Electric Power , 2018 , 35 ( 2 ): 43 - 48 .
龚钢军 , 安晓楠 , 陈志敏 , 等 . 基于SAE-ELM的电动汽车充电站负荷预测模型 [J]. 现代电力 , 2019 , 36 ( 6 ): 9 - 15 .
GONG Gangjun , AN Xiaonan , CHEN Zhimin , et al . Model of load forecasting of electric vehicle charging station based on SAE-ELM [J]. Modern Electric Power , 2019 , 36 ( 6 ): 9 - 15 .
SHI H , XU M , MA Q , et al . A whole system assessment of novel deep learning approach on short-term load forecasting [J]. Energy Procedia , 2017 , 142 : 2791 - 2796 .
SHI H , XU M , LI R . Deep learning household load forecasting—a novel pooling deep RNN [J]. IEEE Transactions on Smart Grid , 2017 , 9 ( 5 ): 5271 - 5280 .
曹有为 , 闫双红 , 刘海涛 , 等 . 基于降噪时序深度学习网络的风电功率短期预测方法 [J]. 电力系统及其自动化学报 , 2020 , 32 ( 1 ): 145 - 150 .
CAO Youwei , YAN Shuanghong , LIU Haitao , et al . Short-term wind power forecasting method based on noise-reduction time-series deep learning network [J]. Proceedings of the CSU-EPSA , 2020 , 32 ( 1 ): 145 - 150 .
郎伟明 , 麻向津 , 周博文 , 等 . 基于LSTM和非参数核密度估计的风电功率概率区间预测 [J]. 智慧电力 , 2020 , 48 ( 2 ): 31 - 37 .
LANG Weiming , MA Xiangjin , ZHOU Bowen , et al . Wind power probabilistic intervals prediction based on LSTM and nonparametric kernel density estimation [J]. Smart Power , 2020 , 48 ( 2 ): 31 - 37 .
赵亮 , 刘友波 , 余莉娜 , 等 . 基于深度信念网络的光伏电站短期发电量预测 [J]. 电力系统保护与控制 , 2019 , 47 ( 18 ): 11 - 19 .
ZHAO Liang , LIU Youbo , YU Lina , et al . Short-term power generation forecast of PV power station based on deep belief network [J]. Power System Protection and Control , 2019 , 47 ( 18 ): 11 - 19 .
唐振浩 , 孟庆煜 , 曹生现 , 等 . 基于小波深度置信网络的风电爬坡预测方法 [J]. 太阳能学报 , 2019 , 40 ( 11 ): 3213 - 3220 .
TANG Zhenhao , MENG Qingyu , CAO Shengxian , et al . Wind power ramp prediction algorithm based on wavelet deep belief network [J]. Acta Energiae Solaris Sinica , 2019 , 40 ( 11 ): 3213 - 3220 .
殷豪 , 欧祖宏 , 陈德 , 等 . 基于二次模式分解和级联式深度学习的超短期风电功率预测 [J]. 电网技术 , 2020 , 44 ( 2 ): 445 - 453 .
YIN Hao , OU Zuhong , CHEN De , et al . Ultra-short-term wind power prediction based on two-layer mode decomposition and cascaded deep learning [J]. Power System Technology , 2020 , 44 ( 2 ): 445 - 453 .
王晨 , 寇鹏 . 基于卷积神经网络和简单循环单元集成模型的风电场内多风机风速预测 [J]. 电工技术学报 , 2020 , 35 ( 13 ): 2723 - 2735 .
WANG Chen , KOU Peng . Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convolutional neural network and simple recurrent unit [J]. Transaction of China Electrotechnical Society , 2020 , 35 ( 13 ): 2723 - 2735 .
闫炯程 , 李常刚 , 刘玉田 . 基于SDAE特征提取的含风电电网可用输电能力计算 [J]. 电力系统自动化 , 2019 , 43 ( 1 ): 32 - 39 .
YAN Jiongcheng , LI Changgang , LIU Yutian . Available transfer capability calculation in power system with wind power based on SDAE feature extraction [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 32 - 39 .
韩佶 , 苗世洪 , 李力行 , 等 . 基于多视角迁移学习的风场内机群划分及等值风场参数综合优化 [J]. 中国电机工程学报 , 2020 , 40 ( 15 ): 4866 - 4880 .
HAN Ji , MIAO Shihong , LI Lixing , et al . Wind turbines clustering in wind farm based on multi-view transfer learning and synthetic optimization of parameters in equivalent wind farm [J]. Proceedings of the CSEE , 2020 , 40 ( 15 ): 4866 - 4880 .
WANG Long , ZHANG Zijun , CHEN Jieqiu . Short-term electricity price forecasting with stacked denoising autoencoders [J]. IEEE Transactions on Power Systems , 2017 , 32 ( 4 ): 2673 - 2681 .
姚子麟 , 张亮 , 邹斌 , 等 . 含有高比例风电的电力市场电价预测 [J]. 电力系统自动化 , 2020 , 44 ( 12 ): 49 - 55 .
YAO Zilin , ZHANG Liang , ZOU Bin , et al . Electricity price prediction for electricity market with high proportion of wind power [J]. Automation of Electric Power Systems , 2020 , 44 ( 12 ): 49 - 55 .
任浩 , 屈剑锋 , 柴毅 , 等 . 深度学习在故障诊断领域中的研究现状与挑战 [J]. 控制与决策 , 2017 , 32 ( 8 ): 1345 - 1358 .
REN Hao , QU Jianfeng , CHAI Yi , et al . Deep learning for fault diagnosis: the state of the art and challenge [J]. Control and Decision , 2017 , 32 ( 8 ): 1345 - 1358 .
石鑫 , 朱永利 , 萨初日拉 , 等 . 基于深度信念网络的电力变压器故障分类建模 [J]. 电力系统保护与控制 , 2016 , 44 ( 1 ): 71 - 76 .
SHI Xin , ZHU Yongli , SA Churila , et al . Power transformer fault classifying model based on deep belief network [J]. Power System Protection and Control , 2016 , 44 ( 1 ): 71 - 76 .
代杰杰 , 宋辉 , 杨祎 , 等 . 基于油中气体分析的变压器故障诊断ReLU-DBN方法 [J]. 电网技术 , 2018 , 42 ( 2 ): 658 - 664 .
DAI Jiejie , SONG Hui , YANG Yi , et al . Dissolved gas analysis of insulating oil for power transformer fault diagnosis based on ReLU-DBN [J]. Power System Technology , 2018 , 42 ( 2 ): 658 - 664 .
刘仲民 , 翟玉晓 , 张鑫 , 等 . 基于DBN-IFCM的变压器故障诊断方法 [J/OL]. 高电压技术 [ 2020-04-08 ]. https://doi.org/10.13336/j.1003-6520.hve.20200310011 https://doi.org/10.13336/j.1003-6520.hve.20200310011 .
LIU Zhongmin , ZHAI Yuxiao , ZHANG Xin , et al . Transformer fault diagnosis method based on DBN-IFCM [J]. High Voltage Engineering [ 2020-04-08 ]. https://doi.org/10.13336/j.1003-6520.hve.20200310011 https://doi.org/10.13336/j.1003-6520.hve.20200310011 .
林刚 , 王波 , 彭辉 , 等 . 基于强泛化卷积神经网络的输电线路图像覆冰厚度辨识 [J]. 中国电机工程学报 , 2018 , 38 ( 11 ): 3393 - 3401 .
LIN Gang , WANG Bo , PENG Hui , et al . Identification of icing thickness of transmission line based on strongly generalized convolutional neural network [J]. Proceedings of the CSEE , 2018 , 38 ( 11 ): 3393 - 3401 .
魏东 , 龚庆武 , 来文青 , 等 . 基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究 [J]. 中国电机工程学报 , 2016 , 36 ( 增刊1 ): 21 - 28 .
WEI Dong , GONG Qingwu , LAI Wenqing , et al . Research on internal and external fault diagnosis and fault-selection of transmission line based on convolutional neural network [J]. Proceedings of the CSEE , 2016 , 36 ( Supplement 1 ): 21 - 28 .
YANG Zhixin , WANG Xiaobo , ZHONG Jianhua . Representational learning for fault diagnosis of wind turbine equipment: a multi-layered extreme learning machines approach [J]. Energies , 2016 , 9 ( 6 ): 1 - 17 .
孙文珺 , 邵思羽 , 严如强 . 基于稀疏自动编码深度神经网络的感应电动机故障诊断 [J]. 机械工程学报 , 2016 , 52 ( 9 ): 65 - 71 .
SUN Wenjun , SHAO Siyu , YAN Ruqiang . Induction motor fault diagnosis based on deep neural network of sparse auto-encoder [J]. Journal of Mechanical Engineering , 2016 , 52 ( 9 ): 65 - 71 .
丛伟 , 胡亮亮 , 孙世军 , 等 . 基于改进深度降噪自编码网络的电网气象防灾方法 [J]. 电力系统自动化 , 2019 , 43 ( 2 ): 42 - 50 .
CONG Wei , HU Liangliang , SUN Shijun , et al . Meteorological disaster prevention method for power grid based on improved stacked denoising auto-encoder network [J]. Automation of Electric Power Systems , 2019 , 43 ( 2 ): 42 - 50 .
TALEBI N , ALI SADRNIA M , DARABI A . Robust fault detection of wind energy conversion systems based on dynamic neural networks [J]. Computational Intelligence and Neuroscience , 2014 , 7 : 1 - 13 .
LIN F , SUN I , YANG K , et al . Recurrent fuzzy neural cerebellar model articulation network fault-tolerant control of six-phase permanent magnet synchronous motor position servo drive [J]. IEEE Transactions on Fuzzy Systems , 2016 , 24 ( 1 ): 153 - 167 .
余涛 , 周斌 , 甄卫国 . 强化学习理论在电力系统中的应用及展望 [J]. 电力系统保护与控制 , 2009 , 37 ( 14 ): 122 - 128 .
YU Tao , ZHOU Bin , ZHEN Weiguo . Application and development of reinforcement learning theory in power systems [J]. Power System Protection and Control , 2009 , 37 ( 14 ): 122 - 128 .
王怀远 , 陈启凡 . 基于代价敏感堆叠变分自动编码器的暂态稳定评估方法 [J]. 中国电机工程学报 , 2020 , 40 ( 7 ): 2213 - 2220 .
WANG Huaiyuan , CHEN Qifan . A transient stability assessment method based on cost-sensitive stacked variational auto-encoder [J]. Proceedings of the CSEE , 2020 , 40 ( 7 ): 2213 - 2220 .
王怀远 , 陈启凡 . 基于堆叠变分自动编码器的电力系统暂态稳定评估方法 [J]. 电力自动化设备 , 2019 , 39 ( 12 ): 134 - 139 .
WANG Huaiyuan , CHEN Qifan . Transient stability assessment method of electric power systems based on stacked variational auto-encoder [J]. Electric Power Automation Equipment , 2019 , 39 ( 12 ): 134 - 139 .
李宝琴 , 吴俊勇 , 邵美阳 , 等 . 基于集成深度置信网络的精细化电力系统暂态稳定评估 [J]. 电力系统自动化 , 2020 , 44 ( 6 ): 17 - 28 .
LI Baoqin , WU Junyong , SHAO Meiyang , et al . Refined transient stability evaluation for power system based on ensemble deep belief network [J]. Automation of Electric Power Systems , 2020 , 44 ( 6 ): 17 - 28 .
邵美阳 , 吴俊勇 , 李宝琴 , 等 . 基于两阶段集成深度置信网络的电力系统暂态稳定评估 [J]. 电网技术 , 2020 , 44 ( 5 ): 1776 - 1787 .
SHAO Meiyang , WU Junyong , LI Baoqin , et al . Transient stability assessment of power system based on two-stage ensemble deep belief network [J]. Power System Technology , 2020 , 44 ( 5 ): 1776 - 1787 .
高昆仑 , 杨帅 , 刘思言 , 等 . 基于一维卷积神经网络的电力系统暂态稳定评估 [J]. 电力系统自动化 , 2019 , 43 ( 12 ): 18 - 26 .
GAO Kunlun , YANG Shuai , LIU Siyan , et al . Transient stability assessment for power system based on one-dimensional convolutional neural network [J]. Automation of Electric Power Systems , 2019 , 43 ( 12 ): 18 - 26 .
杨维全 , 朱元振 , 刘玉田 . 基于卷积神经网络的暂态电压稳定快速评估 [J]. 电力系统自动化 , 2019 , 43 ( 22 ): 46 - 52 .
YANG Weiquan , ZHU Yuanzhen , LIU Yutian . Fast assessment of transient voltage stability based on convolutional neural network [J]. Automation of Electric Power Systems , 2019 , 43 ( 22 ): 46 - 52 .
刘威 , 张东霞 , 王新迎 , 等 . 基于深度强化学习的电网紧急控制策略研究 [J]. 中国电机工程学报 , 2018 , 38 ( 1 ): 109 - 119 .
LIU Wei , ZHANG Dongxia , WANG Xinying , et al . A decision making strategy for generating unit tripping under emergency circumstances based on deep reinforcement learning [J]. Proceedings of the CSEE , 2018 , 38 ( 1 ): 109 - 119 .
余涛 , 周斌 . 电力系统电压/无功控制策略研究综述 [J]. 继电器 , 2008 , 36 ( 6 ): 79 - 85 .
YU Tao , ZHOU Bin . A survey on voltage/reactive power control strategy for power systems [J]. Relay , 2008 , 36 ( 6 ): 79 - 85 .
刁浩然 , 杨明 , 陈芳 , 等 . 基于强化学习理论的地区电网无功电压优化控制方法 [J]. 电工技术学报 , 2015 , 30 ( 12 ): 408 - 414 .
DIAO Haoran , YANG Ming , CHEN Fang , et al . Reactive power and voltage optimization control approach of the regional power grid based on reinforcement learning theory [J]. Transaction of China Electrotechnical Society , 2015 , 30 ( 12 ): 408 - 414 .
李琦 , 乔颖 , 张宇精 . 配电网持续无功优化的深度强化学习方法 [J]. 电网技术 , 2020 , 44 ( 4 ): 1473 - 1480 .
LI Qi , QIAO Ying , ZHANG Yujing . Continuous reactive power optimization of distribution network using deep reinforcement learning [J]. Power System Technology , 2020 , 44 ( 4 ): 1473 - 1480 .
邵美阳 , 吴俊勇 , 石琛 , 等 . 基于数据驱动和深度置信网络的配电网无功优化 [J]. 电网技术 , 2019 , 43 ( 6 ): 1874 - 1885 .
SHAO Meiyang , WU Junyong , SHI Chen , et al . Reactive power optimization of distribution network based on data driven and deep belief network [J]. Power System Technology , 2019 , 43 ( 6 ): 1874 - 1885 .
DUAN Jiajun , SHI Di , DIAO Ruisheng , et al . Deep-reinforcement-learning-based autonomous voltage control for power grid operations [J]. IEEE Transactions on Power Systems , 2020 , 35 ( 1 ): 814 - 817 .
DIAO Ruisheng , WANG Zhiwei , SHI Di , et al . Autonomous voltage control for grid operation using deep reinforcement learning [C]// IEEE PES General Meeting 2019, August 4-8 , 2019 , Atlanta , USA: 1-5 .
YIN L , YU T , ZHOU L , et al . Artificial emotional reinforcement learning for automatic generation control of large-scale interconnected power grids [J]. IET Generation , Transmission & Distribution , 2017 , 11 ( 9 ): 2305 - 2313 .
IMTHIAS A T P , NAGENDRA R P S , SASTRY P S . A reinforcement learning approach to automatic generation control [J]. Electric Power Systems Research , 2002 , 63 ( 1 ): 9 - 26 .
张孝顺 , 李清 , 余涛 , 等 . 基于协同一致性迁移 Q 学习算法的虚拟发电部落AGC功率动态分配 [J]. 中国电机工程学报 , 2017 , 37 ( 5 ): 1455 - 1467 .
ZHANG Xiaoshun , LI Qing , YU Tao , et al . Collaborative consensus transfer Q -learning based dynamic generation dispatch of automatic generation control with virtual generation tribe [J]. Proceedings of the CSEE , 2017 , 37 ( 5 ): 1455 - 1467 .
殷林飞 , 余涛 . 基于深度 Q 学习的强鲁棒性智能发电控制器设计 [J]. 电力自动化设备 , 2018 , 38 ( 5 ): 12 - 19 .
YIN Linfei , YU Tao . Design of strong robust smart generation controller based on deep Q learning [J]. Electric Power Automation Equipment , 2018 , 38 ( 5 ): 12 - 19 .
孙宏斌 , 黄天恩 , 郭庆来 , 等 . 面向调度决策的智能机器调度员研制与应用 [J]. 电网技术 , 2020 , 44 ( 1 ): 1 - 8 .
SUN Hongbin , HUANG Tianen , GUO Qinglai , et al . Automatic operator for decision-making in dispatch: research and applications [J]. Power System Technology , 2020 , 44 ( 1 ): 1 - 8
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 .
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: PartⅠ proposals and fundamentals of guiding learning [J]. Proceedings of the CSEE , 2017 , 37 ( 19 ): 5560 - 5571 .
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 .
尚宇炜 , 郭剑波 , 吴文传 , 等 . 数据-知识融合的机器学习(一):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 .
尚宇炜 , 郭剑波 , 吴文传 , 等 . 数据-知识融合的机器学习(二):泛化风险 [J]. 中国电机工程学报 , 2019 , 39 ( 16 ): 4641 - 4649 .
SHANG Yuwei , GUO Jianbo , WU Wenchuan , et al . Machine learning methods embedded with domain knowledge: Part Ⅱ generalization risk [J]. Proceedings of the CSEE , 2019 , 39 ( 16 ): 4641 - 4649 .
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