As one of the most promising models in unsupervised learning on complex distribution, the generative adversarial network (GAN) model has made outstanding achievements in nonlinear model analysis and image generation [25; 26; 27]. Based on the competitive learning between the generator and the discriminator, this model can adaptively learn the expression paradigm in the input data [28; 29; 30]. The classic GAN model has problems such as training difficulties and mode collapse. [PERSON] introduced the Wasserstein distance to guide the training process, which significantly improved the performance of the GAN model [31; 32]. At present, the time series data-generating ability of the WGAN remains to be studied. Therefore, in this paper, the GRUD-WGAN model is proposed to realize the missing data imputation of the on-site data. Because of the influence of natural inflow conditions and the adjustment requirements of the power grid, the operating parameters of the FTU vary across a wide range and are generally of a high frequency. The monitoring data are highly correlated with operating conditions. Traditional performance evaluation methods of the FTU are primarily based on the overage alarm strategy and the fixed threshold, ignoring the correlation between monitoring data and working condition parameters [33]. To solve this problem, [PERSON] et al. adopted the backpropagation neural network to construct the nonlinear mappingrelationship between operating parameters and the vibration amplitude of the lower bracket [34]. This research realizes the evaluation of the FTU under variable operating conditions. However, the definite numerical mapping relation is susceptible to the random fluctuation of signals. Therefore, the Gaussian process regression (GPR) is introduced to establish a probability mapping model between operating parameters and the probability density distribution of monitored values, so as to improve the robustness of the performance evaluation model against random noise. After quantifying the abstract FTU performance into performance degradation indicators (PDI), the degradation trend prediction problem is essentially transformed into a time series forecasting task. Because RNNs, such as the GRU and the long short-term memory (LSTM) network, can learn the potential timing information, they are widely used in sequence prediction [35; 36; 37; 38]. [PERSON] et al. added the temporal pattern attention (TPA) mechanism based on the LSTM structure to further improve the performance of the LSTM model in mining temporal dependencies [39]. In this paper, the TPA-LSTM is adopted to construct the prediction model for the performance degradation indicator of the FTU. To sum up, in the field of the evaluation and performance prediction of the FTU, there are few studies on the cleaning of low-quality on-site data. The data imputation method of the incomplete data set of the FTU is not yet mature. Meanwhile, few studies have considered the random fluctuation of data in establishing a healthy model of the FTU. In addition, there is room for the further improvement of the accuracy of PDI prediction models. In this paper, an approach for the performance evaluation and prediction of FTU considering low-quality data and variable operating conditions is proposed. The main contributions are highlighted as follows:  
1. Considering the variable operating conditions and the characteristics of the anomaly samples, an on-site data-cleaning method based on DBSCAN is constructed to adaptively detect both singulars and outliers. 2. Combining the incomplete sequence information mining ability of the GRUD and the hidden pattern learning ability of the WGAN, the GRUD-WGAN-based missing value imputation model is proposed to improve the low-quality data utilization value. 3. Based on the GPR, the mapping relationship between the operating parameters and the distribution of monitored data is established as the healthy-state probability model of the FTU.