[1]刘金生,程维杰,陈择栖,等.基于NARX神经网络的孤网自治运行能力评估方法[J].南京理工大学学报(自然科学版),2019,43(06):684-692.[doi:10.14177/j.cnki.32-1397n.2019.43.06.003]
 Liu Jinsheng,Cheng Weijie,Chen Zeqi,et al.Evaluation method of autonomous operation capability ofisolated network based on NARX neural network[J].Journal of Nanjing University of Science and Technology,2019,43(06):684-692.[doi:10.14177/j.cnki.32-1397n.2019.43.06.003]
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基于NARX神经网络的孤网自治运行能力评估方法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
43卷
期数:
2019年06期
页码:
684-692
栏目:
出版日期:
2019-12-31

文章信息/Info

Title:
Evaluation method of autonomous operation capability ofisolated network based on NARX neural network
文章编号:
1005-9830(2019)06-0684-09
作者:
刘金生1程维杰1陈择栖1张俊芳2朱肖镕2柳 伟2任祖怡3
1.深圳供电局有限公司,广东 深圳 518000; 2.南京理工大学 自动化学院,江苏 南京 210094; 3.南京南瑞继保电气有限公司,江苏 南京 211102
Author(s):
Liu Jinsheng1Cheng Weijie1Chen Zeqi1Zhang Junfang2Zhu Xiaorong2Liu Wei2Ren Zuyi3
1.Shenzhen Power Supply Bureau Co.,Ltd.,Shenzhen 518000,China; 2.School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China; 3.Nanjing NR Electric Co.,Ltd.,Nanjing 211102,China
关键词:
非线性有源自回归 神经网络 孤网 自治运行 源荷协调 改进灰关联度分析算法 熵值法 标准化方法
Keywords:
nonlinear auto-regression with exogenous neural networks isolated networks autonomous operation source-load coordination improved grey relational degree analysis method entropy method standardized method
分类号:
TM714.3
DOI:
10.14177/j.cnki.32-1397n.2019.43.06.003
摘要:
为了提高评估准确度,本文提出基于非线性有源自回归(NARX)动态神经网络的孤网自治运行能力综合评估方法。在分析网源荷协调机理的基础上,明确影响孤网自治运行能力的关键因素,包括源源互补、源网协调、源荷协调、网荷互动4个方面。在计及多项关键影响因素的基础上,构建1套可评估孤网自治运行能力的指标体系。使用改进灰关联度分析算法与熵值法对神经网络训练样本进行数据处理分析,以设置NARX神经网络模型。采用基于NARX的孤网自治运行能力综合评估方法进行评估,并利用标准化方法量化评估结果。以某地区典型电网为例,证明该文方法与灰关联度分析方法相比,输出值更贴近目标输出值。
Abstract:
A comprehensive evaluation method of autonomous operation capability of an isolated network is proposed based on the nonlinear auto-regression with exogenous(NARX)dynamic neural network to improve the evaluation accuracy. Based on analyzing the mechanism of network-source-load coordination,the key factors affecting the autonomous operation capability of the isolated network are defined,including four aspects:source-source complementation,source-net coordination,source-load coordination and network-load interaction. Considering several key influencing factors,an indicator system is established to evaluate the autonomous operation capability of the isolated network. Training samples of a neural network are processed and analyzed by using improved grey relational degree analysis method and entropy method,and a NARX neural network model is set. The NARX is used to achieve the evaluation and scoring of the autonomous operation capability of the isolated network,and standardized method is used to quantify the evaluating result. Taking a typical power grid in a given area for example,the results show that compared with the gray relation analysis method,the output of this method is closer to the target output.

参考文献/References:

[1] 肖欣,周渝慧,张宁,等. 城市电力饱和负荷分析技术及其应用研究综述[J]. 电力自动化设备,2014,34(6):146-152.
Xiao Xin,Zhou Yuhui,Zhang Ning,et al. Survey of saturated load analysis technology for urban power system and its application[J]. Electric Power Automation Equipment,2014,34(6):146-152.
[2]于汀,蒲天骄,刘广一,等. 基于柔性直流分区互联的受端城市电网无功电压控制策略[J]. 高电压技术,2017,43(7):50-55.
Yu Ting,Pu Tianjiao,Liu Guangyi,et al. Reactive power and voltage control strategy of receiving-end urban power grid with flexible DC interconnected between partitions[J]. High Voltage Engineering,2017,43(7):50-55.
[3]郭庆来,辛蜀骏,王剑辉,等. 由乌克兰停电事件看信息能源系统综合安全评估[J]. 电力系统自动化,2016,40(5):145-147.
Guo Qinglai,Xin Shujun,Wang Jianhui,et al. Comprehensive security assessment for a cyber physical energy system:A lesson from Ukraine’s blackout[J]. Automation of Electric Power Systems,2016,40(5):145-147.
[4]李俊. 深圳电网4·10大停电事件的处理及启示[J]. 南方电网技术,2014,8(1):65-69.
Li Jun. The processing and inspiration of Shenzhen Power Grid 4·10 blackout event[J]. Southern Power System Technology,2014,8(1):65-69.
[5]易俊,卜广全,郭强,等. 巴西“3·21”大停电事故分析及对中国电网的启示[J]. 电力系统自动化,2019,43(2):1-9.
Yi Jun,Bu Guangquan,Guo Qiang,et al. Analysis on blackout in Brazilian Power Grid on March 21,2018 and its enlightenment to power grid in China[J]Automation of Electric Power Systems,2019,43(2):1-9.
[6]郭力萍,张伟. 基于时序模型的含孤岛运行配电网供电能力评估[J]. 电测与仪表,2017,54(24):110-116.
Guo Liping,Zhang Wei. Evaluation of power supply capability for distribution network with isolated island operation based on time-series model[J]. Electrical Measurement & Instrumentation,2017,54(24):110-116.
[7]张健铭,毕天姝,刘辉,等. 孤网运行与频率稳定研究综述[J]. 电力系统保护与控制,2011,39(11):149-154.
Zhang Jianming,Bi Tianshu,Liu Hui,et al. Review of frequency stability for islanded power system[J]. Power System Protection and Control,2011,39(11):149-154.
[8]黄淼,文旭,刘育明,等. 地区电网孤网运行事件的实例分析[J]. 电力系统保护与控制,2018,46(13):166-170.
Huang Miao,Wen Xu,Liu Yuming,et al. Case analysis for isolated operation of a regional power network[J]. Power System Protection and Control,2018,46(13):166-170.
[9]于文鹏,刘东,余南华,等. 主动配电网的局部自治区域供蓄能力指标及其应用[J]. 电力系统自动化,2014,38(15):44-50.
Yu Wenpeng,Liu Dong,Yu Nanhua,et al. Power supply and storage capacity index of local autonomy area in an active distribution network and its applications[J]. Automation of Electric Power Systems,2014,38(15):44-50.
[10]Platero C A,Martinez S,Sanchez J A,et al. Power system stability of a small sized isolated network supplied by a combined wind-pumped storage generation system:A case study in the Canary islands[J]. Energies,2012,5(7):2351-2369.
[11]徐梅梅,任祖怡,陈建国,等. 基于空间相关性和小波-神经网络的短期风电功率预测模型[J]. 南京理工大学学报,2016,40(3):360-365.
Xu Meimei,Ren Zuyi,Chen Jianguo,et al. Short-term wind power forecasting model based on spatial correlation and wavelet-neural network[J]. Journal of Nanjing University of Science and Technology,2016,40(3):360-365.
[12]翟化欣. 层次分析法和神经网络的电网安全评估[J]. 现代电子技术,2016,39(21):168-171.
Zhai Huaxin. Power grid security assessment combining AHP with neural network[J]. Modern Electronics Technique,2016,39(21):168-171.
[13]王守相,张娜. 基于灰色神经网络组合模型的光伏短期出力预测[J]. 电力系统自动化,2012,36(19):37-41.
Wang Shouxiang,Zhang Na. Short-term output power forecast of photovoltaic based on a grey and neural network hybrid model[J]. Automation of Electric Power Systems,2012,36(19):37-41.
[14]刘瑞叶,黄磊. 基于动态神经网络的风电场输出功率预测[J]. 电力系统自动化,2012,36(11):19-22.
Liu Ruiye,Huang Lei. Wind power forecasting based on dynamic neural networks[J]. Automation of Electric Power Systems,2012,36(11):19-22.
[15]艾解清,高济. 基于Boltzmann学习策略的粒子群算法[J]. 南京理工大学学报,2012,36(3):402-407.
Ai Jieqing,Gao Ji. Particle swarm optimization based on Boltzmann learning strategy[J]. Journal of Nanjing University of Science and Technology,2012,36(3):402-407.

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备注/Memo

备注/Memo:
收稿日期:2019-02-11 修回日期:2019-04-08
作者简介:刘金生(1981-),男,高级工程师,主要研究方向:电力系统安全稳定分析与控制,E-mail:1361712094@qq.com。
引文格式:刘金生,程维杰,陈择栖,等. 基于NARX神经网络的孤网自治运行能力评估方法[J]. 南京理工大学学报,2019,43(6):684-692.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2019-12-31