Monthly
ISSN 1000-7229
CN 11-2583/TM
Electric Power Construction ›› 2018, Vol. 39 ›› Issue (10): 1-11.doi: 10.3969/j.issn.1000-7229.2018.10.001
DAI Yan1, WANG Liuwang1, LI Yuan2, YAN Yong1, HAN Jiajia1, WEN Fushuan2
Online:
2018-10-01
Supported by:
This work is supported by State Grid Corporation of China Research Program (No. 5211DS17002D).
CLC Number:
DAI Yan, WANG Liuwang, LI Yuan, YAN Yong, HAN Jiajia, WEN Fushuan. A Brief Survey on Applications of New Generation Artificial Intelligence in Smart Grids[J]. Electric Power Construction, 2018, 39(10): 1-11.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.cepc.com.cn/EN/10.3969/j.issn.1000-7229.2018.10.001
[1]鞠平, 周孝信, 陈维江, 等. “智能电网+”研究综述[J]. 电力自动化设备, 2018, 38(5): 2-11.
JU Ping, ZHOU Xiaoxin, CHEN Weijiang, et al. “Smart Grid Plus” research overview[J]. Electric Power Automation Equipment, 2018, 38(5): 2-11.
[2]董朝阳, 赵俊华, 文福拴, 等. 从智能电网到能源互联网: 基本概念与研究框架[J]. 电力系统自动化, 2014, 38(15): 1-11.
DONG Zhaoyang, ZHAO Junhua, WEN Fushuan, et al. From smart grid to energy internet: Basic concept and research framework[J]. Automation of Electric Power Systems, 2014, 38(15): 1-11.
[3]王钦, 蒋怀光, 文福拴, 等. 智能电网中大数据的概念、技术与挑战[J]. 电力建设, 2016, 37(12): 1-10.
WANG Qin, JIANG Huaiguang, WEN Fushuan, et al. Concept, technology and challenges of big data in smart grids[J]. Electric Power Construction, 2016, 37(12): 1-10.
[4]AREL I, ROSE D C, KARNOWSKI T P. Deep machine learning-a new frontier in artificial intelligence research[J]. IEEE Computational Intelligence Magazine, 2010, 5(4): 13-18.
[5]中国国务院. 新一代人工智能发展规划[EB/OL].[2018-07-08].http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[6]LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[7]KOBER J, BAGNELL J A, PETERS J. Reinforcement learning in robotics: A survey[J]. International Journal of Robotics Research, 2013, 32(11): 1238-1274.
[8]PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge & Data Engineering, 2010, 22(10): 1345- 1359.
[9]HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
[10]HU B, LU Z, LI H, et al. Convolutional neural network architectures for matching natural language sentences[J]. Advances in Neural Information Processing Systems, 2015, 3: 2042-2050.
[11]朱乔木, 李弘毅, 王子琪, 等. 基于长短期记忆网络的风电场发电功率超短期预测[J]. 电网技术, 2017, 41(12): 3797-3802.
ZHU Qiaomu, LI Hongyi, WANG Ziqi, et al. Short-term wind power forecasting based on LSTM[J]. Power System Technology, 2017, 41(12): 3797-3802.
[12]GEHRING J, MIAO Y, METZE F, et al. Extracting deep bottleneck features using stacked auto-encoders[C]// IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2013: 3377-3381.
[13]WATKINS C, DAYAN P. Q-learning[J]. Machine Learning, 1992, 8(3-4): 279-292.
[14]WU J, HE H, PENG J, et al. Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus[J]. Applied Energy, 2018, 222: 799-811.
[15]VELUSAMY B, ANOUNCIA S M, ABRAHAM G.SaGe framework - mapping of SARSA to adaptive e-learning using learning styles[J]. International Journal of Engineering & Technology, 2013, 5(2): 2306- 2314.
[16]DAI W, YANG Q, XUE G R, et al. Boosting for transfer learning[C]// International Conference on Machine Learning. ACM, 2007: 193-200.
[17]DAI W, XUE G R, YANG Q, et al. Co-clustering based classification for out-of-domain documents[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2007: 210-219.
[18]DAI W, YANG Q, XUE G R, et al. Self-taught clustering[C]// International Conference on Machine Learning.ACM, 2008: 200-207.
[19]中国国务院新闻办公室. 新一代人工智能具有五大特点[EB/OL].[2017-07-21].http://www.scio.gov.cn/32344/32345/35889/36946/zy36950/Document/1559026/1559026.htm.
[20]WANG H Z, WANG G B, LI G Q, et al. Deep belief network based deterministic and probabilistic wind speed forecasting approach[J]. Applied Energy, 2016, 182: 80-93.
[21]史佳琪, 张建华. 基于深度学习的超短期光伏精细化预测模型研究[J]. 电力建设, 2017, 38(6): 28-35.
SHI Jiaqi, ZHANG Jianhua. Ultra short-term photovoltaic refined forecasting model based on deep learning[J]. Electric Power Construction, 2017, 38(6): 28-35.
[22]WANG H Z, YI H Y, PENG J C, et al. Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network[J]. Energy Conversion & Management, 2017, 153: 409-422.
[23]KHODAYAR M, KAYNAK O, KHODAYAR M E. Rough deep neural architecture for short-term wind speed forecasting[J]. IEEE Transactions on Industrial Informatics, 2017, 13(6): 2770-2779.
[24]QURESHI A S, KHAN A, ZAMEER A, et al. Wind power prediction using deep neural network based meta regression and transfer learning[J]. Applied Soft Computing, 2017, 58:742-755.
[25]黎静华, 黄乾, 韦善阳, 等. 基于S-BGD和梯度累积策略的改进深度学习方法及其在光伏出力预测中的应用[J]. 电网技术, 2017, 41(10): 3292-3300.
LI Jinghua, HUANG Qian, WEI Shanyang, et al. Improved deep learning algorithm based on S-BGD and gradient pile strategy and its applications in PV power forecasting[J]. Power System Technology, 2017, 41(10): 3292-3300.
[26]刘国静, 韩学山, 王尚, 等. 基于强化学习方法的风储合作决策[J]. 电网技术, 2016, 40(9): 2729-2736.
LIU Guojing, HAN Xueshan, WANG Shang, et al. Optimal decision-making in the cooperation of wind power and energy storage based on reinforcement learning algorithm[J]. Power System Technology, 2016, 40(9): 2729-2736.
[27]朱乔木, 陈金富, 李弘毅, 等. 基于堆叠自动编码器的电力系统暂态稳定评估[J]. 中国电机工程学报, 2018, 38(10): 2937-2946.
ZHU Qiaomu, CHEN Jinfu, LI Hongyi, et al. Power system transient stability assessment based on stacked automatic encoder[J]. Proceedings of the CSEE, 2018, 38(10): 2937-2946.
[28]朱乔木, 党杰, 陈金富, 等. 基于深度置信网络的电力系统暂态稳定评估方法[J]. 中国电机工程学报, 2018, 38(3): 735-743.
ZHU Qiaomu, DANG Jie, CHEN Jinfu, et al. A method for power system transient stability assessment based on deep belief networks[J]. Proceedings of the CSEE, 2018, 38(3): 735-743.
[29]周悦, 谭本东, 李淼, 等. 基于深度学习的电力系统暂态稳定评估方法[J]. 电力建设, 2018, 39(2): 103-108.
ZHOU Yue, TAN Bendong, LI Miao, et al. Transient stability assessment of power system based on deep learning technology[J]. Electric Power Construction, 2018, 39(2): 103-108.
[30]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.
[31]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, 347.
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, 347.
[32]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.
[33]江浩荣, 徐茂鑫, 王克英. 网格化知识迁移学习算法及其在碳能复合流优化中的应用[J]. 电力建设, 2017, 38(7): 96-105.
JIANG Haorong, XU Maoxin, WANG Keying. Grid knowledge transfer learning algorithm and its application in carbon-energy combined-flow optimization[J]. Electric Power Construction, 2017, 38(7): 96-105.
[34]CHEN Y, HUANG S, LIU F, et al. Evaluation of reinforcement learning based false data injection attack to automatic voltage control[J]. IEEE Transactions on Smart Grid, 2018, PP(99): 1-12.
[35]HE Y, MENDIS G J, WEI J. Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism[J]. IEEE Transactions on Smart Grid, 2017, 8(5): 2505-2516.
[36]YANG L, LI Y, LI Z. Improved-ELM method for detecting false data attack in smart grid[J]. International Journal of Electrical Power & Energy Systems, 2017, 91: 183-191.
[37]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.
[38]韩传家, 张孝顺, 余涛, 等. 风险调度中引入知识迁移的细菌觅食强化学习优化算法[J]. 电力系统自动化, 2017, 41(8): 69-77, 97.
HAN Chuanjia, ZHANG Xiaoshun, YU Tao, et al. Optimization algorithm of reinforcement learning based knowledge transfer bacteria foraging for risk dispatch[J]. Automation of Electric Power Systems, 2017, 41(8): 69-77, 97.
[39]程乐峰, 余涛, 张孝顺, 等. 信息–物理–社会融合的智慧能源调度机器人及其知识自动化: 框架、技术与挑战[J]. 中国电机工程学报, 2018, 38(1): 25-40.
CHENG Lefeng, YU Tao, ZHANG Xiaoshun, et al. Cyber-physical-social systems based smart energy robotic dispatcher and its knowledge automation: Framework, techniques and challenges[J]. Proceedings of the CSEE, 2018, 38(1): 25-40.
[40]鲁守银, 张营, 李建祥, 等. 移动机器人在高压变电站中的应用[J]. 高电压技术, 2017, 43(1): 276-284.
LU Shouyin, ZHANG Ying, LI Jianxiang, et al. Application of mobile robot in high voltage substation[J]. High Voltage Engineering, 2017, 43(1): 276-284.
[41]施孟佶, 秦开宇, 李凯, 等. 高压输电线路多无人机自主协同巡线设计与测试[J]. 电力系统自动化, 2017, 41(10): 117-122.
SHI Mengji, QIN Kaiyu, Li Kai, et al. Design and testing on autonomous multi-UAV cooperation for high-voltage transmission line inspection[J]. Automation of Electric Power Systems, 2017, 41(10): 117-122.
[42]王万国, 田兵, 刘越, 等. 基于RCNN的无人机巡检图像电力小部件识别研究[J]. 地球信息科学学报, 2017, 19(2): 256-263.
WANG Wanguo, TIAN Bing, LIU Yue, et al. Study on the electrical devices detection in UAV images based on region based convolutional neural networks[J]. Journal of Geo-information Science, 2017, 19(2): 256-263.
[43]李军锋, 王钦若, 李敏. 结合深度学习和随机森林的电力设备图像识别[J]. 高电压技术, 2017, 43(11): 3705-3711.
LI Junfeng, WANG Qinruo, LI Min. Electric equipment image recognition based on deep learning and random forest[J]. High Voltage Engineering, 2017, 43(11): 3705-3711.
[44]高强, 阳武, 李倩. DBN层次趋势研究及其在航拍图像故障识别中的应用[J]. 仪器仪表学报, 2015, 36(6): 1267-1274.
GAO Qiang, YANG Wu, LI Qian. Research on deep belief network layer tendency and its application into identifying fault images of aerial images[J]. Chinese Journal of Scientific Instrument, 2015, 36(6): 1267-1274.
[45]林颖, 郭志红, 陈玉峰. 基于卷积递归网络的电流互感器红外故障图像诊断[J]. 电力系统保护与控制, 2015, 43(16): 87-94.
LIN Ying, GUO Zhihong, CHEN Yufeng. Convolutional-recursive network based current transformer infrared fault image diagnosis[J]. Power System Protection and Control, 2015, 43(16): 87-94.
[46]刘梓权, 王慧芳, 曹靖, 等. 基于卷积神经网络的电力设备缺陷文本分类模型研究[J]. 电网技术, 2018, 42(2): 644-651.
LIU Ziquan, WANG Huifang, CAO Jing, et al. Power equipment defect text classification model based on convolutional neural network[J]. Power System Technology, 2018, 42(2): 644-651.
[47]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, 2017, 36(S1): 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, 2017, 36(S1): 21-28.
[48]刘辉海, 赵星宇, 赵洪山, 等. 基于深度自编码网络模型的风电机组齿轮箱故障检测[J]. 电工技术学报, 2017, 32(17): 156-163.
LIU Huihai, ZHAO Xingyu, ZHAO Hongshan, et al. Fault detection of wind turbine gearbox based on deep autoencoder network[J]. Transactions of China Electrotechnical Society, 2017, 32(17): 156-163.
[49]赵洪山, 刘辉海. 基于性能改善深度置信网络的风电机组主轴承状态分析[J]. 电力自动化设备, 2018, 38(2): 44-49.
ZHAO Hongshan, LIU Huihai. Condition analysis of wind turbine main bearing based on deep belief network with improved performance[J]. Electric Power Automation Equipment, 2018, 38(2): 44-49.
[50]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, 2017, 40(2): 658-664.
DAI Jiejie, SONG Hui, YANG Yi, et al. Dissolved gas analysis of insulating oil for power transformer fault diagnosis with ReLU-DBN[J]. Power System Technology, 2017, 40(2): 658-664.
[51]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 forecasting based on deep belief network[J]. Automation of Electric Power Systems, 2018, 42(5): 133-139.
[52]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.
[53]EVANS R,GAO J.Deepmind AI reduces google data centre cooling bill by 40%[EB/OL].(2016-07-20)[2018-07-27]. https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/.
[54]KONG W C, DONG Z Y, JIA Y W, et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2017, PP(99): 1-11.
[55]KONG W C, DONG Z Y, HILL D J, et al. Short-term residential load forecasting based on residentbehaviour learning[J]. IEEE Transactions on Power Systems, 2018, 33(1): 1087-1088.
[56]SHI H, XU M, LI R. Deep learning for household load forecasting-a novel pooling deep RNN[J].IEEE Transactions on Smart Grid, 2018, 9(5): 5271-5280.
[57]史佳琪, 谭涛, 郭经, 等. 基于深度结构多任务学习的园区型综合能源系统多元负荷预测[J]. 电网技术, 2018, 42(3): 698-706.
SHI Jiaqi, TAN Tao, GUO Jing, et al. Multi-task learning based on deep architecture for various types of load forecasting in regional energy system integration[J]. Power System Technology, 2018, 42(3): 698-706.
[58]包涛, 张孝顺, 余涛, 等. 反映实时供需互动的Stackelberg博弈模型及其强化学习求解[J]. 中国电机工程学报, 2018, 38(10): 2947-2956.
BAO Tao, ZHANG Xiaoshun, YU Tao, et al. A Stackelberg game model of real-time supply-demand interaction and the solving method via reinforcement learning[J]. Proceedings of the CSEE, 2018, 38(10): 2947-2956.
[59]RUELENS F, CLAESSENS B J, VANDAEL S, et al. Residential demand response of thermostatically controlled loads using batch reinforcement learning[J]. IEEE Transactions on Smart Grid, 2017, 8(5): 2149-2159.
[60]LOPEZ K L, GAGNE C, GARDNER M A. Demand-side management using deep learning for smart charging of electric vehicles[J]. IEEE Transactions on Smart Grid, 2018, PP(99): 1-9.
[61]CHIS A, LUNDEN J, KOIVUNEN V. Reinforcement learning-based plug-in electric vehicle charging with forecasted price[J]. IEEE Transactions on Vehicular Technology, 2017, 66(5): 3674-3684.
[62]WANG L, ZHANG Z, CHEN J. Short-term electricity price forecasting with stackeddenoising autoencoders[J]. IEEE Transactions on Power Systems, 2017, 32(4): 2673-2681.
[63]邓丽. 基于机器学习的智能电网实时电价研究[D]. 沈阳: 沈阳理工大学, 2016.
DENG Li. Research on the real-time pricing in smart grid based on machine learning[D].Shenyang: Shenyang Ligong University, 2016.
[64]白青贵. 电力批发市场中基于强化学习的参与者行为特性研究[D]. 长沙: 湖南大学, 2013.
BAI Qinggui. Using reinforcement learning to study the features of the participants behavior in wholesale power market[D]. Changsha: Hunan University, 2013.
[65]曾嘉志, 赵雄飞, 李静, 等. 用电侧市场放开下的电力市场多主体博弈[J]. 电力系统自动化, 2017, 41(24): 129-136.
ZENG Jiazhi, ZHAO Xiongfei, LI Jing, et al. Game among multiple entities in electricity market with liberalization of power demand side market[J]. Automation of Electric Power Systems, 2017, 41(24): 129-136.
[66]KOZAN B, ZLATAR I, PARAVAN D, et al. The advanced bidding strategy for power generators based on reinforcement learning[J]. Energy Sources Part B Economics Planning & Policy, 2014, 9(1): 79-86.
[67]颜拥, 赵俊华, 文福拴, 等. 能源系统中的区块链: 概念、应用与展望[J]. 电力建设, 2017, 38(2): 12-20.
YAN Yong, ZHAO Junhua, WEN Fushuan, et al. Blockchain in energy systems: Concept, application and prospect[J]. Electric Power Construction, 2017, 38(2): 12-20. |
[1] | YUAN Lüzerui, GU Jie,JIN Zhijian. User-Side Data Application Framework Based on Cloud-Edge-User Collaboration in Power Internet of Things [J]. Electric Power Construction, 2020, 41(7): 1-8. |
[2] | QI Bing, YE Xin, LI Bin, CHEN Songsong, LI Yuanfei, SHI Kun. Research on the Architecture of User-Side Power Internet of Things Considering IEC Standards [J]. Electric Power Construction, 2020, 41(5): 92-99. |
[3] | ZHANG Yajian, YANG Ting, MENG Guangyu. Review and Prospect of Ubiquitous Power Internet of Things in Smart Distribution System [J]. Electric Power Construction, 2019, 40(6): 1-12. |
[4] | FU Zhixin, LI Xiaoyi, YUAN Yue. Research on Key Technologies of Ubiquitous Power Internet of Things [J]. Electric Power Construction, 2019, 40(5): 1-12. |
[5] | YANG Yinan, QI Linhai, WANG Hong, SU Linping. Research on Generation Technology of Small Sample Data Based on Generative Adversarial Network [J]. Electric Power Construction, 2019, 40(5): 71-77. |
[6] | LI Bin1, ZHANG Jie1, TIAN Shiming2, DONG Mingyu2, QI Bing1, SUN Yi1, ZHU Weiyi3. Recent Progress and Trend Analysis of Standardization of Smart Grid User Domain [J]. Electric Power Construction, 2018, 39(3): 12-. |
[7] | ZHANG Zhe, QI Donglian, ZHANG Jianliang. Distributed Optimization Method for Energy Management in Smart Grid Based on Three-level Game Model [J]. Electric Power Construction, 2018, 39(10): 28-36. |
[8] | LI Bin, ZHOU Qiuyan, CHEN Songsong, CUI Gaoying, QI Bing, SUN Yi. Data Analysis Model for Demand Response System and Its Application [J]. Electric Power Construction, 2017, 38(9): 88-. |
[9] | CAO Junwei, YANG Jie, YUAN Zhongda, WU Koulin, FANG Taixun, YANG Fei. Review of Intelligent Power Electronic Device Research [J]. Electric Power Construction, 2017, 38(5): 18-. |
[10] |
ZHANG Pei,HE Yi,ZHANG Dahai,SUN Yixin,CHENG Jiaxu.
Judgment Rules of Big Data Application in Power Industry
|
[11] | LI Li, ZHU Yongli, SONG Yaqi . Stream Computing and Dynamic Visualization for Electric Power Equipment Monitoring Data [J]. Electric Power Construction, 2017, 38(5): 91-. |
[12] | DONG Zhaoyang, CHEN Yingying, LUO Fengji. Innovative Data-Driven Applications in Future Active Distribution Network: Technologies, Prospect and Challenges [J]. ELECTRIC POWER CONSTRUCTION, 2017, 38(5): 2-. |
[13] |
ZHAO Dongyuan, GAO Feng.
Impact of Energy Internet on Power Demand Side Management Evolution
|
[14] | HE Jinghan,LU Yuzi,LU Jinyao,HU Bo,YANG Fang, HE Bo. User Classification Method Based on ‘Evolution’ PCA and Its Application [J]. Electric Power Construction, 2017, 38(3): 101-. |
[15] | LIU Dunnan, TANG Tianqi, ZHAO Jiawei, YE Bin, MA Jing, WANG Bao, YANG Min. Big Energy Data Information Service Pricing and Its Application in Electricity Market [J]. Electric Power Construction, 2017, 38(2): 52-. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
Copyright @ ELECTRIC POWER CONSTRUCTION Editorial Office
Address: Tower A225, SGCC, Future Science & Technology Park,Beijing, China Postcode:102209 Telephone:010-66602697
Technical support: Beijing Magtech Co.ltd support@magtech.com.cn