Method for Power Grid Digital Operation Data Integration Based on K-Medoids Clustering with Support for Real-Time Cross-Modal Applications.

Data in power grid digital operation exhibit multisource heterogeneous characteristics, resulting in low integration efficiency and slow anomaly detection response. To address this, this paper proposes a method for power grid digital operation data integration based on K-medoids clustering. The basic service layer utilizes an Field Programmable Gate Array parallel architecture. This enables millisecond-level synchronous acquisition and dynamic preprocessing of multisource data, such as mechanical vibration, partial discharge signals, and temperature. The implementation is based on the analysis of the power grid digital operation structure. The data are then fed back to the cloud service layer, which, through business integration services, data analysis, and data access services, performs data filtering and analysis. Subsequently, the data are input to the application layer via the database server. The application layer employs a K-medoids clustering method that introduces a density-weighted Euclidean distance metric and an adaptive centroid selection strategy, significantly enhancing the clustering performance of multisource data. In particular, the proposed architecture supports real-time data processing and can be extended to cross-modal scenarios, including integration with speech-to-text systems in power grid monitoring. By aligning with low-latency neural network principles, this method facilitates timely decision-making in intelligent operation environments. Experiments confirm the method's efficacy. It acquires and integrates multisource heterogeneous power grid digital operation data effectively. The data throughput of different power grid digital operation data sources all exceed 110 MB/s. The silhouette coefficient of the integrated data sets is greater than 0.91, indicating that the integration of power grid digital operation data using this method exhibits good separability and reliability, enabling rapid detection of data anomalies within the power grid, thus laying a solid foundation for the operation and maintenance management of power grid digital operation.