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In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detected, recognized adaptively and cleaned. Further combining the superiority of the WSGAIN-GP model in data filling, the missing values in the cleaned data are automatically generated by the unsupervised learning of the features and the distribution of real monitoring data. The validation analysis is carried out by the online monitoring dataset of the actual operating units, and the comparison experime ....

Generative Adversarial Networks Gans , Wasserstein Generative Adversarial Networks Wgans , Slim Generative Adversarial Imputation Network , Generative Adversarial Imputation Network , Wasserstein Generative Adversarial Imputation Network , Wasserstein Generative Adversarial Networks , Wasserstein Slim Generative Adversarial Imputation Network , Generative Adversarial Networks , Density Based Spatial Clustering , Noise Applications , Local Outlier Factor Algorithm , Density Estimation Based Clustering , On Point Sorting Clustering Methods , Hierarchical Density Based Spatial Clustering , Gradient Penalty , Reachable Distance , Spanning Tree Construction , Hierarchical Clustering Structure , Silhouette Coefficient Index , Hint Generator , Hint Matrix , Methodology Flow , Hydropower Unit Condition Monitoring Data Based , Hellinger Distance , Root Mean Square Error ,