基于深度学习的新能源爬坡事件预测方法

doi:10.6040/j.issn.1672-3961.0.2019.132

Abstract

Abstract:

With the gradual increase of the renewable energy penetration rate, the ramp event that caused the unbalanced active power occured sometimes, and even a large load loss. Due to the insufficient accuracy of wind power and photovoltaic prediction, there were many operational scenarios to be considered. The time domain simulation could not meet the online assessment requirements. A method based on deep learning was proposed in this paper. Considering the generation unit and tie line adjustment ability, the stacked denoising autoencoder was used to extract each layer feature to train support vector machine. The wind power, photovoltaic and load forecast data, and the power of the tie line at the previous moment were taken as inputs, and whether the ramp event occured as an output. The vector machine was used to quickly predict whether a ramp event occured. The simulation results of practical power grid showed that the proposed method was fast and accurate. It could effectively identify ramp events.

Key words: power system, deep learning, denoising autoencoder, support vector machine, ramp event

CLC Number:

Zhixiang LIANG,Xiaoming LIU,Ying MU,Yutian LIU. Prediction method of wind power and PV ramp event based on deep learning[J].Journal of Shandong University(Engineering Science), 2019, 49(5): 24-28.

Figures/Tables 5

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

References 16

1	LIU Y , FAN R , TERZIJA V . Power system restoration: a literature review from 2006 to 2016[J]. Journal of Modern Power System and Clean Energy, 2016, 4 (3): 332- 341. doi: 10.1007/s40565-016-0219-2
2	马欢, 李常刚, 刘玉田. 风电爬坡事件多级区间预警方法[J]. 电力系统自动化, 2017, 41 (11): 39- 47. doi: 10.7500/AEPS20161021008
	MA Huan , LI Changgang , LIU Yutian . Multi-level early warning method for wind power ramp events[J]. Automation of Electric Power Systems, 2017, 41 (11): 39- 47. doi: 10.7500/AEPS20161021008
3	ELA E, KIRBY B. ERCOT event on February 26, 2008: lessons learned[R]. Golden Colorado, USA: National Renewable Energy Laboratory (NREL), 2008.
4	FERREIRA C, GAMA J, MATIAS L, et al. A survey on wind power ramp forecasting[R]. Illinois, USA: Argonne National Laboratory (ANL), 2011.
5	李军徽, 冯喜超, 严干贵, 等. 高风电渗透率下的电力系统调频研究综述[J]. 电力系统保护与控制, 2018, 46 (2): 163- 170.
	LI Junhui , FENG Xichao , YAN Gangui , et al. Survey on frequency regulation technology in high wind penetration power system[J]. Power System Protection and Control, 2018, 46 (2): 163- 170.
6	ZHENG H , KUSIAK A . Prediction of wind farm power ramp rates: a data-mining approach[J]. Journal of Solar Energy Engineering, 2009, 131 (3): 376- 385.
7	ZAREIPOUR H, HUANG D, ROSEHART W.Wind power ramp events classification and forecasting: a data mining approach[C]//2011 IEEE Power & Energy Society General Meeting.San Diego, America: IEEE, 2011: (1-3).
8	崔明建, 孙元章, 柯德平, 等. 考虑电网侧频率偏差的风电功率爬坡事件预测方法[J]. 电力系统自动化, 2014, 38 (5): 8- 13.
	CUI Mingjian , SUN Yuanzhang , KE Deping , et al. Prediction method for wind power ramp events considering frequency deviation of power grid side[J]. Automation of Electric Power Systems, 2014, 38 (5): 8- 13.
9	宋豪, 宋曙光, 王超, 等. 抽水蓄能电站对山东电网风电接纳能力的影响[J]. 山东大学学报(工学版), 2011, 41 (5): 138- 142.
	SONG Hao , SONG Shuguang , WANG Chao , et al. Impacts of the pumped storage power station on the wind generation integration capability of the Shandong Power Grid[J]. Journal of Shandong University(Engineering Science), 2011, 41 (5): 138- 142.
10	梁亮, 李普明, 刘嘉宁, 等. 抽水蓄能电站自主调频控制策略研究[J]. 高电压技术, 2015, 41 (10): 3288- 3295.
	LIANG Liang , LI Puming , LIU Jianing , et al. Study on the control strategy of pumped storage power station for frequency regulation[J]. High Voltage Engineering, 2015, 41 (10): 3288- 3295.
11	代杰杰, 宋辉, 杨祎, 等. 基于栈式降噪自编码器的输变电设备状态数据清洗方法[J]. 电力系统自动化, 2017, 41 (12): 224- 230. doi: 10.7500/AEPS20161201003
	DAI Jiejie , SONG Hui , YANG Wei , et al. Cleaning method for status data of power transmission and transformation equipment based on stacked denoising autoencoders[J]. Automation of Electric Power Systems, 2017, 41 (12): 224- 230. doi: 10.7500/AEPS20161201003
12	尹雪燕, 闫炯程, 刘玉田, 等. 基于深度学习的暂态稳定评估与严重度分级[J]. 电力自动化设备, 2018, 38 (5): 64- 69.
	YIN Xueyan , YAN Jiongcheng , LIU Yutian , et al. Deep learning based transient stability assessment and severity grading[J]. Electric Power Automation Equipment, 2018, 38 (5): 64- 69.
13	田芳, 周孝信, 于之虹. 基于支持向量机综合分类模型和关键样本集的电力系统暂态稳定评估[J]. 电力系统保护与控制, 2017, 45 (22): 1- 8. doi: 10.7667/PSPC161864
	TIAN Fang , ZHOU Xiaoxin , YU Zhihong . Power system transient stability assessment based on comprehensive SVM classification model and key sample set[J]. Power System Protection and Control, 2017, 45 (22): 1- 8. doi: 10.7667/PSPC161864
14	别朝红, 王锡凡. 蒙特卡洛法在评估电力系统可靠性中的应用[J]. 电力系统自动化, 1997, 21 (6): 68- 75. doi: 10.3321/j.issn:1000-1026.1997.06.021
	BIE Zhaohong , WANG Xifan . The application of Monte Carlo method to reliability evaluation of power systems[J]. Automation of Electric Power Systems, 1997, 21 (6): 68- 75. doi: 10.3321/j.issn:1000-1026.1997.06.021
15	何成明, 王洪涛, 王春义, 等. 风电功率爬坡事件作用下考虑时序特性的系统风险评估[J]. 电力自动化设备, 2016, 36 (1): 35- 41.
	HE Chengming , WANG Hongtao , WANG Chunyi , et al. System risk assessment considering timing characteristics under wind power ramp event[J]. Electric Power Automation Equipment, 2016, 36 (1): 35- 41.
16	薛志英, 周明, 李庚银. 大规模风电接入电力系统备用决策评述[J]. 电力系统保护与控制, 2013, 41 (4): 148- 155.
	XUE Zhiying , ZHOU Ming , LI Gengyin . Survey on reserve decision of power systems with large scale wind power integration[J]. Power System Protection and Control, 2013, 41 (4): 148- 155.

Related Articles 15

[1]	LI Changgang, LI Baoliang, CAO Yongji, WANG Jiaying. Review and prospect on artificial intelligence application in power system power flow calculation [J]. Journal of Shandong University(Engineering Science), 2025, 55(5): 1-17.
[2]	ZHOU Qunying, SUI Jiacheng, ZHANG Ji, WANG Hongyuan. Industrial product surface defect detection based on self supervised convolution and parameter free attention mechanism [J]. Journal of Shandong University(Engineering Science), 2025, 55(4): 40-47.
[3]	XUE Bingbing, WANG Yong, YANG Weihao, WANG Chuan, YU Di, WANG Xu. Real-time expressway traffic data imputation and state prediction based on ETC system data [J]. Journal of Shandong University(Engineering Science), 2025, 55(3): 58-71.
[4]	DONG Mingshu, CHEN Liqi, MA Chuanyi, ZHANG Zhuhao, SUN Renjuan, GUAN Yanhua, ZHUANG Peizhi. Deep learning-based intelligent judgment for radar detection of pavement cracks [J]. Journal of Shandong University(Engineering Science), 2025, 55(3): 72-79.
[5]	ZHANG Hengxu, MA Ruicong, CAO Yongji, LIU Yimin, ZOU Shihao. Review and prospect of research on new power system synchronous stability [J]. Journal of Shandong University(Engineering Science), 2025, 55(2): 1-15.
[6]	Jiachun LI,Bowen LI,Jianbo CHANG. An efficient and lightweight RGB frame-level face anti-spoofing model [J]. Journal of Shandong University(Engineering Science), 2023, 53(6): 1-7.
[7]	Yue YUAN,Yanli WANG,Kan LIU. Named entity recognition model based on dilated convolutional block architecture [J]. Journal of Shandong University(Engineering Science), 2022, 52(6): 105-114.
[8]	Tongyu JIANG, Fan CHEN, Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction [J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8.
[9]	Jianqing WU,Xiuguang SONG. Review on development of simultaneous localization and mapping technology [J]. Journal of Shandong University(Engineering Science), 2021, 51(5): 16-31.
[10]	Xueshan HAN,Xinyi WANG,Ming YANG,Yixiao YU. Review and prospect of renewable energy ramp events [J]. Journal of Shandong University(Engineering Science), 2021, 51(5): 53-62.
[11]	YANG Xiuyuan, PENG Tao, YANG Liang, LIN Hongfei. Adaptive multi-domain sentiment analysis based on knowledge distillation [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 15-21.
[12]	Qingfa CHAI,Shoujing SUN,Jifu QIU,Ming CHEN,Zhen WEI,Wei CONG. Prediction method of power grid emergency supplies under meteorological disasters [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 76-83.
[13]	LIAO Jinping, MO Yuchang, YAN Ke. Model and application of short-term electricity consumption forecast based on C-LSTM [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 90-97.
[14]	Guoyong CAI,Xinhao HE,Yangyang CHU. Visual sentiment analysis based on spatial attention mechanism and convolutional neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 8-13.
[15]	LIU Shuai, WANG Lei, DING Xutao. Emotional EEG recognition based on Bi-LSTM [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 35-39.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LAI Xiang . The global domain of attraction for a kind of MKdV equations[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 87 -92 .
[2]	CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[3]	. [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 131 -136 .
[4]	ZHANG Ying-Chun, WANG Zuo-Xun, WANG Gui-Juan. High voltage cable temperature measurement system based on neural network controller[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 62 -67 .
[5]	ZHU Xiang-cai,LUAN Yun-cai,XU Jian . An urban traffic evaluation system based on VB and FTA[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(4): 89 -92 .
[6]	FANG Wei,JIANG Chang-sheng,QIAN Cheng-shan . H_∞ fuzzy tracking control of a class of uncertain nonlinear systems with time-varying delay[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(5): 47 -52 .
[7]	ZHOU Xin-hong,PENG Yu-hua,LIU Yong,QU Huai-jing . Image retrieval based on multi-scale analysis and SVM relevance feedback[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(2): 45 -50 .
[8]	LI Shu-cai,WANG Zhao-qing,LI Shu-chen . A polygonal finite element method based on irrational function interpolation[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(2): 66 -70 .
[9]	WANG Huiqing, SUN Hongwei, ZHANG Jianhui. Time series similarity searching algorithm based on Map/Reduce[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(1): 15 -21 .
[10]	HAO Ming-hui,WANG Xi-ping,WANG Min,ZHOU Shen-jie . The solution of a oneedge crack of a finite plate with the influence of couple stress in a uniform tension field [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(2): 92 -95 .

方法	15 min准确率/%	1 h准确率/%
时域仿真法	100	100
SVM	97.63	97.67
SDAE(最高阶隐层特征)+SVM	97.70	97.18
SDAE(所有隐层特征)+SVM	98.52	98.28

Prediction method of wind power and PV ramp event based on deep learning

RichHTML

PDF (PC)