Data Imputation of Wind Turbine Using Generative Adversarial Nets with Deep Learning Models

Abstract

Data missing problem is one of the most important issues in the field of wind turbine (WT). The missing data can lead to many problems that negatively affect the safety of power system and cause economic loss. However, under some complicated conditions, the WT data changes according to different environments, which would reduce the efficiency of some traditional data interpolation methods. In order to solve this problem and improve data interpolation accuracy, this paper proposed a WT data imputation method using generative adversarial nets (GAN) with deep learning models. First, conditional GAN is used as the framework to train the generative network. Then convolutional neural network is applied for both the generative model and the discriminative model. Through the zero-sum game between the two models, the imputation model can be well trained. Due to the deep learning models, the trained data imputation model can effectively recover the data with a few parameters of the input data. A case study based on real WT SCADA data was conducted to verify the proposed method. Two more data imputation methods were used to make the comparison. The experiments results showed that the method proposed in this paper is effective.