Ultra-short-term Power Prediction Model Considering Spatial-Temporal Characteristics of Offshore Wind Turbines

被引：0
|
作者：
Lin Z. [1 ]
Liu K. [1 ]
Shen F. [1 ]
Zhao X. [2 ]
Liang Y. [1 ]
Dong M. [1 ]
机构：
[1] Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming
[2] Yunnan Electric Power Grid Co., Ltd., Kunming
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 23期
基金：
中国国家自然科学基金;
关键词：
deep learning; dynamic time warping; offshore wind power; spatial-temporal characteristic; ultra-short-term power prediction;
D O I：
10.7500/AEPS20211124004
中图分类号：
学科分类号：
摘要：
The single-unit prediction of offshore wind power cannot quickly predict the overall power of an offshore wind farm, and uneven fluctuations among units lead to poor quality of cluster power curves and low prediction accuracy. Therefore, this paper proposes an ultra-short-term power prediction model considering the spatial-temporal characteristics of offshore wind turbines. First, the improved dynamic time warping (DTW) algorithm is used to quantify the spatial-temporal characteristic similarity of offshore wind turbines and analyze the spatial-temporal characteristics of offshore wind turbines. Then, the Transformer model based on deep learning is used to establish the offshore wind power prediction model. Finally, the spatial-temporal characteristic similarity of offshore wind turbines and the bus position information are comprehensively considered to cluster the offshore wind turbines and conduct the ultra-short-term power prediction. The analysis of measured data of offshore wind turbines show that the proposed method can effectively quantify and measure the spatial-temporal characteristics of offshore wind turbines and timely predict the ultra-short-term power of offshore wind turbine groups. © 2022 Automation of Electric Power Systems Press. All rights reserved.
引用
收藏
页码：59 / 66
页数：7
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