Open Access

ARTICLE

A Wind Power Prediction Framework for Distributed Power Grids

by Bin Chen¹, Ziyang Li¹, Shipeng Li¹, Qingzhou Zhao¹, Xingdou Liu^2,*

1 Power Grid Design Institute, Shandong Electric Power Engineering Consulting Institute Co., Ltd., Jinan, 250013, China
2 School of Electrical Engineering, Shandong University, Jinan, 250012, China

* Corresponding Author: Xingdou Liu. Email:

Energy Engineering 2024, 121(5), 1291-1307. https://doi.org/10.32604/ee.2024.046374

Received 28 September 2023; Accepted 08 December 2023; Issue published 30 April 2024

Abstract

To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve, and historical power is reconstructed using variational mode decomposition (VMD) filtering to form input variables in chronological order. Finally, input variables of a single turbine enter the temporal convolutional network (TCN) to complete initial feature extraction, and then integrate the outputs of all TCN layers using Long Short Term Memory Networks (LSTM) to obtain power prediction sequences for all turbine positions. The proposed method was tested on a wind farm connected to a distributed power grid, and the results showed it to be superior to existing typical methods.

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Keywords

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