Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

Wenlong Liao; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai; Dechang Yang; Yusen Wang

DOI:10.35833/MPCE.2020.000894


Data-driven Missing Data Imputation for Wind Farms Using Context Encoder



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Author: Wenlong Liao¹,Birgitte Bak-Jensen¹,Jayakrishnan Radhakrishna Pillai¹,Dechang Yang^2,1,Yusen Wang³
Author Affiliation: 1.the Department of Energy Technology, Aalborg University, Aalborg, Denmark;2.the College of Information and Electrical Engineering, China Agricultural University, Beijing, China;3.the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
Foundation:	This work was supported by the China Scholarship Council.

Abstract:	High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.
Keywords:	Data-driven ; missing data imputation ; wind farm ; deep learning ; context encoder

Received:December 23, 2020 Online Time:2022/07/15



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