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首页> 外文期刊>IEEE sensors journal >A Two-Stage Deep Autoencoder-Based Missing Data Imputation Method for Wind Farm SCADA Data
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A Two-Stage Deep Autoencoder-Based Missing Data Imputation Method for Wind Farm SCADA Data

机译：基于两阶段的深度自动化器缺失数据撤销方法，用于风电场SCADA数据

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摘要

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 in 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.
机译：本文提出了一种基于深度非参数模型，稀疏自动化器（SAE）和基于梯度的优化算法，坐标血统（CD），提出了一种新的两级方法缺失数据的复杂模式，即同时发生的相关属性（DLCA）中的数据丢失，专注于并研究。在本文中，缺失的数据载体被制定为两级优化问题。在第一阶段，SAE的重建误差（RE）被认为是通过完整的数据集进行训练非参数属性重建模型以学习低维歧管，其中数据被密集地分布。在第二阶段，RE用作优化基于开发的SAE的类似但不完整的数据集的缺失数据载体的目标函数。根据res的潜在凸起关于缺失属性的归纳，这通过初步实验经验被发现，应用CD算法以有效解决优化问题。通过使用大型风力涡轮机数据集验证了所提出的方法的功效。计算实验的结果表明，所提出的方法在考虑众所周知的基准中表现出良好的，该基准是众所周知的缺失数据。

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