Hierarchical Layered Method of Converter Station Based on Principal Component Analysis and Association Analysis

Abstract

The HVDC has entered a period of rapid development in China. As the core of HVDC transmission, the converter station has the characteristics of many types of equipment, various equipment, various auxiliary systems and large signal levels, and it has a high requirement for real-time monitoring signals. To realize the remote monitoring and control of the converter station, it is urgent to solve the problem of stratification and classification of the remote transmission signal. Therefore, this paper mainly focuses on the remote transmission signals of large converter stations, and conducts stratification and classification strategies from the three business perspectives of converter station remote monitoring, DC system status identification, and event intelligent diagnosis and processing. First of all, this paper investigates the research status of stratification and classification of converter station signals, and combs and collects the remote transmission signals. Then, combined with the domestic high-voltage DC protection system and the actual operation data of the telecontrol system, the stratification and classification strategies of the far-transmission signal of the converter station are studied from three different angles. Finally, a differentiated signal stratification and classification method for different services based on grey correlation analysis and Apriori correlation analysis was proposed. The actual case verification can get the result. The signal stratification and classification method proposed in this paper can accurately filter the signals which used to evaluate the status of the converter station. The sets of signal can be used to generate a signal importance list for different devices.