人工智能技术在智能电网中的应用分析和展望

doi:10.11930/j.issn.1004-9649.201711221

摘要/Abstract

摘要： 人工智能技术的进步和突破,对于电网智能化程度的提高具有重要意义。从智能电网内涵出发,梳理电网发展的智能化需求,结合各类人工智能技术的特征和适用性,设计了具体的应用场景,开展了关键技术应用的SWOT分析,并对可能的困难进行估计。指出：许多人工智能技术在智能电网各环节的规划、预测、辅助决策、智能控制、视频监控、巡检、故障诊断等应用场景普遍具有重要参考价值,神经网络、专家系统、数据挖掘等技术各有其优劣和适用范围,应用中需要重视各类技术的科学选择,并对可靠性、可解释性、数据样本积累、基础设施准备、知识库的修正维护、机密性等可能的挑战有所准备。

关键词: 智能电网, 人工智能, 神经网络, 专家系统, 卷积神经网络, 态势分析法（SWOT）

Abstract: The progresses and breakthroughs of artificial intelligence technologies are of great significance to improvement of intelligence level of smart grid. starting from connotations of smart grid, combined with characteristics and applicable scopes of all kinds of artificial intelligence technologies, the intelligence needs of smart grid are combed. Possible application scenes are designed, a SWOT analysis on some key artificial intelligence technologies is carried out and possible difficulties are estimated. It is pointed out that, many artificial intelligence technologies have important reference values in the smart grid applications, including planning, prediction, auxiliary decision-making, intelligent control, video surveillance and inspection fault diagnosis. neural networks, expert systems, data mining and other technologies all have their own advantages, disadvantages and applicable scopes. we should pay enough attention to scientific choice of all kinds of technologies, and prepare for possible challenges such as reliability, interpretability, accumulation of data samples, revision and maintenance of knowledge base, infrastructure preparation and confidentiality.

Key words: smart grid, artificial intelligence(AI), neural networks, expert system, convolutional neural network, strengths weaknesses opportunities threats (SWOT)

中图分类号:

TM761

李博, 高志远. 人工智能技术在智能电网中的应用分析和展望[J]. 中国电力, 2017, 50(12): 136-140.

LI Bo, GAO Zhiyuan. Analysis and Prospect on the Application of Artificial Intelligence Technologies in Smart Grid[J]. Electric Power, 2017, 50(12): 136-140.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201711221

https://www.electricpower.com.cn/CN/Y2017/V50/I12/136

参考文献

[1] 肖世杰. 构建中国智能电网技术思考[J]. 电力系统自动化,2009,33（9）：1-4.
XIAO Shijie. Consideration of technology for construction chinese smart grid [J]. Automation of Electric Power Systems, 2009, 33(9): 1-4.
[2] 中山元,顾立强. 日本智能电网的动向[J]. 中国电力,2011,44（1）：41-44.
ZHONG Shanyuan, GU Liqiang. Trend of Japanese smart grid [J]. Electric Power, 2011, 44(1): 41-44.
[3] 白明月,刘甲男,张雪萍,等. 欧盟智能电网发展及启示[J]. 中国电力,2012,45（1）：6-9.
BAI Mingyue, LIU Jianan, ZHANG Xueping, et al . Development and enlightenment of smart grid in EU [J]. Electric Power, 2012, 45(1): 6-9.
[4] 何大愚. 关于未来智能电网的特征[J]. 中国电力,2012,45（2）：9-11.
HE Dayu. Features of future smart grids[J]. Electric Power, 2012, 45(2): 9-11.
[5] 李博,高志远,曹阳. 智能电网支撑智慧城市关键技术[J]. 中国电力,2015,48（11）：123-130.
LI Bo, GAO Zhiyuan, CAO Yang. Review of key technology of smart grid supporting smart city construction[J]. Electric Power, 2015, 48(11): 123-130.
[6] 李开复,王咏刚. 人工智能[M]. 北京：文化发展出版社,2017.
[7] 钟义信. 人工智能：概念、方法、机遇[J]. 科学通报,2017,62（22）：2473-2479.
ZHONG Yixin. Artificial intelligence: concept, approach and opportunity[J]. Chinese Science Bulletin, 2017, 62(22): 2473-2479.
[8] 中国国务院. 新一代人工智能发展规划[EB/OL]. （2017-07-20）[2017-07-26]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[9] 韩力群. 人工神经网络理论、设计及应用[M]. 北京：化学工业出版社,2007.
[10] 王文杰,史忠植. 人工智能原理辅导与练习[M]. 北京：清华大学出版社,2007.
[11] YANN Lecun, YOSHUA Bengio, GEOFFREY Hinton. Deep learning[EB/OL]. （2015-05-28）[2017-08-10]. http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf
[12] GEOFFREY E Hinton, SIMON Osindero, YEE Whye Teh. A fast learning algorithm for deep belief nets[J]. Neural Computation,2006, 18(7): 1527-1554.
[13] YOSHUA Bengio, PASCAL Lamblin, DAN Popovici, et al . Greedy layer-wise training of deep networks[C]// Proceedings of the 19th International Conference on Neural Information Processing Systems. 2006, Cambridge, MA, USA.
[14] ITMAR Arel, DEREK C Rose, THOMAS P Karnowski. Deep machine learning-A new frontier in artificial intelligence research[J]. IEEE Computational Intelligence Magazine, 2010(11): 13-18.
[15] 史继莉,邱晓燕. 人工智能在最优潮流中的应用综述[J]. 继电器,2005,33（16）：85-95.
SHI Jili, QIU Xiaoyan. Artificial intelligence applications in optimal power flow [J]. Relay, 2005, 33(16): 85-95.
[16] 盛戈皞,涂光瑜,罗毅. 人工智能技术在电力系统无功电压控制中的应用[J]. 电网技术,2002,26（6）: 22-27.
SHENG Gehao, TU Guangyu, LUO Yi. Application of artificial intelligence techniques in reactive power/voltage control of power system [J]. Power System Technology, 2002, 26(6): 22-27.
[17] 毕天姝,倪以信,杨奇逊. 人工智能技术在输电网络故障诊断中的应用述评[J]. 电力系统自动化,2000,24（2）：11-16.
BI Tianshu, NI Yixin, YANG Qixun. An evaluation of artificial intelligent technologies for fault diagnosis in power system[J].Automation of Electric Power Systems, 2000, 24(2)：11-16.
[18] 廖志伟,孙雅明,叶青华. 人工智能技术在电力系统故障诊断中应用[J]. 电力系统及其自动化学报,2003,15（6）：71-79.
LIAO Zhiwei, SUN Yaming, YE Qinghua. Artificial intelligent technologies for fault diagnosis in power system [J]. Proceedings of the EPSA, 2003, 15(6): 71-79.
[19] 王同文,管霖,张尧. 人工智能技术在电网稳定评估中的应用综述[J]. 电网技术,2009,33（12）：60-65.
WANG Tongwen, GUAN Lin, ZHANG Yao. A survey on application of artificial intelligence technology in power system stability assessment[J]. Power System Technology, 2009, 33(12): 60-65.
[20] 韩祯祥,文福拴,张琦. 人工智能在电力系统中的应用[J]. 电力系统自动化,2000,24（2）：2-10.
HAN Zhenxiang, WEN Fushuan, ZHANG Qi. Artificial intelligence application in power systems[J]. Automation of Electric Power Systems, 2000, 24(2): 2-10.
[21] 黄民烈,朱小燕. 期待人工智能突破鲁棒性可解释性局限[N]. 中国电子报,2017-09-01（7）.