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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 gansWasserstein generative adversarial networks wgansSlim generative adversarial imputation networkGenerative adversarial imputation networkWasserstein generative adversarial imputation networkWasserstein generative adversarial networksWasserstein slim generative adversarial imputation networkGenerative adversarial networksDensity based spatial clusteringNoise applicationsLocal outlier factor algorithmDensity estimation based clusteringOn point sorting clustering methodsHierarchical density based spatial clusteringGradient penaltyReachable distance