A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets

Abstract

The limited fault information caused by small fault data samples is a major problem in wind turbine (WT) fault detection. This paper proposes a small-sample WT fault detection method with the synthetic fault data using generative adversarial nets (GANs). First, based on prior knowledge, a rough fault data generation process is developed to transform the normal data to the rough fault data. Second, a rough fault data refiner is developed by GANs to make the rough fault data more similar with the real fault data. Moreover, to make the generated data better suited to the WT conditions, GANs are improved in both the generative model and the discriminative model. Third, artificial intelligence (AI)-based WT fault detection models can be well trained by using only the generated data in the condition of small fault data sample. Finally, three groups of generated data evaluation experiments and four groups of WT fault detection comparative experiments are conducted using real WT data collected from a wind farm in northern China. The results indicate that the method proposed in this paper is effective.