Please wait a minute...
发电技术  2018, Vol. 39 Issue (2): 106-111    DOI: 10.12096/j.2096-4528.pgt.2018.017
能源互联网 本期目录 | 过刊浏览 |
人工智能在电力系统中的应用研究与实践综述
朱永利1(),尹金良2,*()
1 新能源电力系统国家重点实验室(华北电力大学), 河北省 保定市 071003
2 天津理工大学电气电子工程学院, 天津市 西青区 300384
Review of Research and Practice of Artificial Intelligence Application in Power Systems
Yongli ZHU1(),Jinliang YIN2,*()
1 The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University), Baoding 071003, Hebei Province, China
2 School of Electrical and Electronic Engineering, Tianjin University of Technology, Xiqing District, Tianjin 300384, China
全文: HTML    PDF(3811 KB)  
输出: BibTeX | EndNote (RIS)      
摘要: 

人工智能在电力系统中的广泛应用,提高了电力系统的安全性、可靠性和运行能力。按早期、中期和后期3个阶段对相关工作进行了总结,包括20世纪八九十年代专家系统、神经网络等的应用情况,并重点介绍了21世纪初出现的相关向量机的特点、原理及其在电力系统中的应用研究现状。

关键词 电力系统人工智能专家系统相关向量机    
Abstract

Artificial intelligence was widely used in power systems. It has improved the security, reliability and operation ability of power systems. The related work in the early, middle and late stages was summarized including expert system and neural network and so on in the 1980s and 1990s and relevance vector machine in the early 21st century. The characteristics, principles and application in power systems of relevant vector machines were mainly introduced.

Key wordspower system    artificial intelligence    expert system    relevance vector machine
收稿日期: 2018-02-03      出版日期: 2018-07-27
基金资助:国家自然科学基金项目(51677072)
通讯作者: 尹金良     E-mail: yonglipw@163.com;yinjinliang2007@126.com
Corresponding author: Jinliang YIN     E-mail: yonglipw@163.com;yinjinliang2007@126.com
作者简介: 朱永利(1963),男,教授,博士生导师,研究方向为网络化监控与智能信息处理、电力设备监测大数据处理, yonglipw@163.com
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
朱永利
尹金良
引用本文:

朱永利,尹金良. 人工智能在电力系统中的应用研究与实践综述[J]. 发电技术, 2018, 39(2): 106-111.
Yongli ZHU,Jinliang YIN. Review of Research and Practice of Artificial Intelligence Application in Power Systems. Power Generation Technology, 2018, 39(2): 106-111.

链接本文:

http://www.pgtjournal.com/CN/10.12096/j.2096-4528.pgt.2018.017      或      http://www.pgtjournal.com/CN/Y2018/V39/I2/106

图1  SVM和RVM用于synthetic分类
图2  分层贝叶斯模型
图3  MKL-RVM原理示意图
1 薛禹胜,刘觉岑,文辉.专家系统在电力系统中的应用-特点,现状和展望[J].电力系统自动化, 1989-03, 13(2): 10-19.
2 张志英.人工智能在电网调度中的应用-日调度计划安排及操作命令票专家系统[D].北京:华北电力学院硕士论文, 1985.
3 朱永利, 杨以涵, 张文勤, 等. 专家系统在编写变电站倒闸操作票中的应用[J]. 中国电机工程学报, 1988, 8 (6): 61- 65.
4 朱永利, 栗然, 杨以涵, 等. 电网调度操作票专家系统[J]. 电力系统及其自动化学报, 1991, 3 (1): 68- 74.
5 王德生. 专家系统在电网调度操作票中的应用[J]. 东北电力技术, 1996, (10): 43- 46.
6 Y Zhu , YH Yang , et al. An expert system for power systems fault analysi[J]. IEEE Trans.On Power Systems, 1994, 9 (1): 503- 509.
doi: 10.1109/59.317573
7 Zhu Yongli , Hogg B W , Zhang W Q , et al. Hybrid expert system for aiding dispatchers on bulk power systems restoration[J]. International Journal of Electrical Power & Energy Systems, 1994, 16 (4): 259- 268.
8 Tipping M E . The relevance vector machine[J]. Advances in Neural Information Processing Systems, 2001, (12): 652- 658.
9 Bishop C M, Tipping M E. Variational relevance vector machines[C]//Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 2000: 46-53.
10 Tipping M E . Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, (1): 211- 244.
11 Psorakis I , Damoulas T , Girolami M A . Multiclass relevance vector machines:sparsity and accuracy[J]. IEEE Transactions on Neural Networks, 2010, 21 (10): 1588- 1598.
doi: 10.1109/TNN.2010.2064787
12 Damoulas T, Ying Y, Girolami M A, et al. Inferring sparse kernel combinations and relevance vectors: an application to subcellular localization of proteins[C]//In Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA2008), San Diego, USA, 2008.
13 Damoulas T , Girolami M A . Probabilistic multi-class multi-kernel learning:on protein fold recognition and remote homology detection[J]. Bioinformatics, 2008, 24 (10): 1264- 1270.
doi: 10.1093/bioinformatics/btn112
14 Damoulas T , Girolami M A . Combining feature spaces for classification[J]. Pattern Recognition, 2009, 42 (11): 2671- 2683.
doi: 10.1016/j.patcog.2009.04.002
15 尹金良, 朱永利, 俞国勤. 相关向量机及其在变压器故障诊断中的应用[J]. 电力自动化设备, 2012, 32 (8): 130- 134.
16 孙曙光, 于晗, 杜太行, 等. 基于振动信号样本熵和相关向量机的万能式断路器分合闸故障诊断[J]. 电工技术学报, 2017, 32 (7): 20- 30.
17 李海英, 刘中银, 宋建成. 电力系统静态安全状态实时感知的相关向量机法[J]. 中国电机工程学报, 2015, 35 (2): 294- 301.
18 尹金良, 朱永利, 俞国勤. 基于多分类相关向量机的变压器故障诊断新方法[J]. 电力系统保护与控制, 2013, 41 (5): 77- 82.
doi: 10.7667/j.issn.1674-3415.2013.05.014
19 律方成, 金虎, 王子建, 等. 基于主成分分析和多分类相关向量机的GIS局部放电模式识别[J]. 电工技术学报, 2015, 30 (6): 225- 231.
20 刘嘉蔚,李奇,陈维荣,等.基于多分类相关向量机和模糊C均值聚类的有轨电车用燃料电池系统故障诊断方法[J/OL].中国电机工程学报: [2018-03-24]. http://kns.cnki.net/KCMS/detail/11.2107.TM.20170824.1554.003.html.
21 朱永利, 尹金良. 组合核相关向量机在电力变压器故障诊断中的应用研究[J]. 中国电机工程学报, 2013, 33 (22): 68- 74.
22 尚海昆, 苑津莎, 王瑜, 等. 多核多分类相关向量机在变压器局部放电模式识别中的应用[J]. 电工技术学报, 2014, 29 (11): 221- 228.
doi: 10.3969/j.issn.1000-6753.2014.11.027
[1] 朱永利, 石鑫, 王刘旺. 人工智能在电力系统中应用的近期研究热点介绍[J]. 发电技术, 2018, 39(3): 204-212.
[2] 周博文,陈麒宇,杨东升. 巴西大停电的思考[J]. 发电技术, 2018, 39(2): 97-105.
[3] 李本新,韩学山,蒋哲,李文博. 计及网损的快速经济调度方法[J]. 发电技术, 2018, 39(1): 90-95.