Structural Design of Operational Analysis and State Modeling for Power Secondary System

With the continuous expansion of the monitoring scope of power secondary systems and the continuous improvement of network security requirements, many safety equipment and business systems are operating in the system. They produce a large amount of raw security data and operation monitoring data. At present, these data are isolated from each other. it makes control of the system's overall operational status and security risks a challenge. In order to realize the monitoring of the overall operating status of power secondary systems and cross-asset collaborative security data mining analysis. This paper presents a non-supervised machine learning based power system analysis and state modeling. First, design a set of asset model rules for secondary power systems. It builds the host equipment, network equipment, security equipment, and business system itself running in the existing business system into an organic whole. Afterwards, we used intelligent machine learning techniques to “draw” the “contours” of normal operation of each business. It provides a contour model for overall system operation analysis and threat warning. Finally, based on this model and real-time analysis technology, we aggregate the data distributed in various business systems in real time. It provides comprehensive support for operation monitoring and safety protection of power secondary systems.