Multi-Source Information Acquisition and Modeling Technology of Converter Station for Fault Intelligent Diagnosis

The DC converter station comprises numerous secondary devices, each with a complex operating principle. When faults occur, these devices generate significant amounts of fault data. However, due to the heterogeneity of these data sources, the information can vary greatly, necessitating the rapid integration and collection of multi-source data from each device for effective analysis. This paper presents a technology for acquiring and modeling multi-source information aimed at facilitating intelligent fault diagnosis in converter stations. Initially, the heterogeneous information from the current DC protection equipment is collected and classified, with a focus on designing supplementary collection methods for various protection data, particularly addressing missing information, as well as establishing an uploading scheme for the main station. Subsequently, utilizing the IEC 61850 standard, SDDI, and GGIO modeling methods, a comprehensive information model is developed for holographic data measurement, sorting, and standardization within the converter station's control and protection system. Ultimately, this information model is integrated into a fault intelligent diagnosis simulation system. The results from the fault diagnosis indicate that the proposed information model effectively meets the data requirements necessary for diagnosing faults in the converter station.