Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 24-28.doi: 10.6040/j.issn.1672-3961.0.2019.132

• Engineering—Special Topic on Artificial Intelligence Application • Previous Articles     Next Articles

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

Zhixiang LIANG1(),Xiaoming LIU2,Ying MU2,Yutian LIU1   

  1. 1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University), Jinan 250061, Shandong, China
    2. Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, Shandong, China
  • Received:2019-04-02 Online:2019-10-20 Published:2019-10-18
  • Supported by:
    承接全球能源互联网的省级大受端电网发展规划及安全防御技术研究;国家重点研发计划项目(2017YFB0902600);国家电网公司科技资助项目(SGJS0000DKJS1700840)

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: 

  • TM7

Fig.1

Training process of DAE"

Fig.2

Pre-training process of SDAE"

Fig.3

Schematic diagram of SDAE and SVM model"

Fig.4

Imbalanced active power at different times"

Table 1

Performance comparison between methods"

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
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