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

Yining Wang, Da Xie, Xitian Wang and Yu Zhang
Additional contact information
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

Energies, 2018, vol. 11, issue 11, 1-19

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.

Keywords: interaction between grid and wind turbine; long short-term memory; wind power prediction; principal component analysis; deep learning; oscillation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/11/3221/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/11/3221/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:11:p:3221-:d:184192

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().