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Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

2023-11-24

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

基于改进型 CEEMDAN 方法和生成式对抗插值网络的风电数据缺失插值模型

Lingyun Zhao1,Zhuoyu Wang1,Tingxi Chen1,Shuang Lv2,Chuan Yuan3,Xiaodong Shen1,Youbo Liu1

1.College of Electrical Engineering,Sichuan University,Chengdu 610065,P.R.China

2.State Grid Sichuan Electric Power Company Chengdu Power Supply Company,610065 P.R.China

3.State Grid Sichuan Electric Power Company,Chengdu 610065,P.R.China

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Abstract

Randomness and fluctuations in wind power output may cause changes in important parameters (e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors (such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

Keywords

Wind power data repair; Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN);Generative adversarial interpolation network (GAIN)

Fig. 1 Wind power anomaly data

Fig. 2 EEMD decomposition results

Fig. 3 ICEEMDAN decomposition results

Fig. 4 Permutation entropy of two decomposition arithmetic subsequences

Fig. 5 Main structure of GAIN

Fig. 6 Imputation result

Fig. 7 ICEEMDAN-GAIN flow chart

Fig. 8 Imputation result

本文引文信息

Zhao LY, Wang CY, Chen XT, et al. (2023) Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network, Global Energy Interconnection, 6(5): 517-529

赵凌云，王茁宇，陈亭希等 (2023) 基于改进型 CEEMDAN 方法和生成式对抗插值网络的风电数据缺失插值模型. 全球能源互联网（英文）, 6(5): 517-529

Biographies

Lingyun Zhao

is currently pursuing the master’s degree in electrical engineering with Sichuan University.The main research directions are new energy prediction and artificial intelligence and their applications in power systems.

Zhuoyu Wang

is currently pursuing the undergraduate degree in electrical engineering with Sichuan University.The main research directions are artificial intelligence and their applications in power systems.

Tingxi Chen

is currently pursuing the undergraduate degree in electrical engineering with Sichuan University.The main research directions are new energy prediction and artificial intelligence and their applications in power systems.

Shuang Lv

is Working at State Grid Sichuan Electric Power Company,The position is an electrical engineer.The main research direction is the application of artificial intelligence technology in power systems.

Chuan Yuan

is Working at State Grid Sichuan Electric Power Company,The position is an electrical engineer.The main research direction is distribution network planning and the application of artificial intelligence technology in power systems.

Xiaodong Shen

graduated from Sichuan University and currently serves as an associate professor and master’s supervisor at Sichuan University.The main research directions are new energy and electricity price prediction,intelligent distribution network planning and operation,artificial intelligence and its application in power systems.

Youbo Liu

graduated from Sichuan University,currently serves as a professor and doctoral supervisor at Sichuan University,and is a reserve for academic and technical leaders in Sichuan Province.Mainly engaged in research in the fields of artificial intelligence in power systems,low-carbon electricity markets,distributed energy and energy storage,and active distribution networks.

编辑：王彦博

审核：王伟

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