Application of Transformer in anomaly indicators forecasting of hydropower units

Abstract

The workloads of routine repair， maintenance， and abnormality detection of hydropower units are heavy. Therefore， traditional manual monitoring may leave out or misjudge abnormalities. Deep learning algorithms are used for data modeling and monitoring abnormalities to reduce costs and improve safety and reliability. With the help of the Transformer neural networks， the efficient and accurate modeling capacity of long-term sequences and the GAN （generative adversarial network） architecture data are used to generate a training strategy. A TransGAN model is used for generative modeling of the measured data of hydropower units and proactively detects abnormal data points. The TransGAN model achieves a detection accuracy rate of 97.76% and a recall rate of 99.23% in hydropower data measurement. The anomaly detection delay is less than 0.1 s. The real-time high-precision anomaly monitoring function is realized.

Keywords

• hydropower units
• anomaly detection
• dimensionality reduction
• transformer
• gan