Data-Driven Generative Model Aimed to Create Synthetic Data for the Long-Term Forecast of Gas Turbine Operation

Abstract

The prediction of gas turbine (GT) future health state plays a strategic role in the current energy sector. However, training an accurate prognostic model is challenging in case of limited historical data (e.g., new installation). Thus, this paper develops a generative adversarial network (GAN) model aimed to generate synthetic data that can be used for data augmentation. The GAN model includes two neural networks, i.e., a generator and a discriminator. The generator aims to generate synthetic data that mimic the real data. The discriminator is a binary classification network. During the training process, the generator is optimized to fool the discriminator in distinguishing between real and synthetic data. The real data employed in this paper were taken from the literature, gathered from three GTs, and refer to two quantities, i.e., corrected power output and compressor efficiency, which are tracked during several years. Three different analyses are presented to validate the reliability of the synthetic dataset. First, a visual comparison of real and synthetic data is performed. Then, two metrics are employed to quantitively evaluate the similarity between real and synthetic data distributions. Finally, a prognostic model is trained by only using synthetic data and then employed to predict real data. The results prove the high reliability of the synthetic data, which can be thus exploited to train a prognostic model. In fact, the prediction error of the prognostic model on the real data is lower than 2.5% even in the case of long-term prediction.