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Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model

Author

Listed:
  • Yining Wang

    (School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai 200240, China)

  • Da Xie

    (School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai 200240, China)

  • Xitian Wang

    (School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai 200240, China)

  • Yu Zhang

    (Shanghai Electric Power Company, Shanghai 200122, China)

Abstract

The interaction between the gird and wind farms has significant impact on the power grid, therefore prediction of the interaction between gird and wind farms is of great significance. In this paper, a wind turbine-gird interaction prediction model based on long short term memory (LSTM) network under the TensorFlow framework is presented. First, the multivariate time series was screened by principal component analysis (PCA) to reduce the data dimensionality. Secondly, the LSTM network is used to model the nonlinear relationship between the selected sequence of wind turbine network interactions and the actual output sequence of the wind farms, it is proved that it has higher accuracy and applicability by comparison with single LSTM model, Autoregressive Integrated Moving Average (ARIMA) model and Back Propagation Neural Network (BPNN) model, the Mean Absolute Percentage Error (MAPE) is 0.617%, 0.703%, 1.397% and 3.127%, respectively. Finally, the Prony algorithm was used to analyze the predicted data of the wind turbine-grid interactions. Based on the actual data, it is found that the oscillation frequencies of the predicted data from PCA-LSTM model are basically the same as the oscillation frequencies of the actual data, thus the feasibility of the model proposed for analyzing interaction between grid and wind turbines is verified.

Suggested Citation

  • Yining Wang & Da Xie & Xitian Wang & Yu Zhang, 2018. "Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model," Energies, MDPI, vol. 11(11), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3221-:d:184192
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    References listed on IDEAS

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    1. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
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    Cited by:

    1. Peng Qian & Xiange Tian & Jamil Kanfoud & Joash Lap Yan Lee & Tat-Hean Gan, 2019. "A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
    2. Ning Li & Fuxing He & Wentao Ma, 2019. "Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p -Power Error Loss," Energies, MDPI, vol. 12(4), pages 1-19, February.
    3. Mansoor Khan & Tianqi Liu & Farhan Ullah, 2019. "A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis," Energies, MDPI, vol. 12(12), pages 1-21, June.

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