A Two-Stage Deep Autoencoder-Based Missing Data Imputation Method for Wind Farm SCADA Data
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 10933-10945 |
Journal / Publication | IEEE Sensors Journal |
Volume | 21 |
Issue number | 9 |
Online published | 22 Feb 2021 |
Publication status | Published - 1 May 2021 |
Link(s)
Abstract
This paper proposes a novel two-stage method for imputing missing SCADA data of wind turbines with high accuracy based on deep nonparametric models, sparse autoencoders (SAE), and a gradient-based optimization algorithm, coordinate descent (CD). A complex pattern of missing data, namely, data loss of correlated attributes (DLCA) that occurs simultaneously, is focused on and studied. In this paper, the missing data imputation is formulated as a two-stage optimization problem. In the first stage, the reconstruction error (RE) of SAE is regarded as the loss for training nonparametric attribute reconstruction models via a complete dataset to learn a low-dimensional manifold, in which data are densely distributed. At the second stage, RE serves as an objective function for optimizing the missing data imputation of a similar but incomplete dataset based on the developed SAE. According to the potential convexity of REs with respect to the imputation of missing attributes, which is empirically discovered through preliminary experiments, the CD algorithm is applied to efficiently solve the optimization problem. The efficacy of the proposed method is validated by using a large real wind turbine dataset. The results of the computational experiments demonstrate that the proposed method performs well on considered benchmarks that are well known for imputing missing data.
Research Area(s)
- Data mining, wind energy, missing data imputation, Autoencoders, neural networks
Citation Format(s)
A Two-Stage Deep Autoencoder-Based Missing Data Imputation Method for Wind Farm SCADA Data. / Liu, Xin; Zhang, Zijun.
In: IEEE Sensors Journal, Vol. 21, No. 9, 01.05.2021, p. 10933-10945.
In: IEEE Sensors Journal, Vol. 21, No. 9, 01.05.2021, p. 10933-10945.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review