Asset Management of Smart Grid using Digital Twin Technology and Machine Learning Algorithms

Abstract

In order to solve the problem of data insecurity in the current smart grid asset monitoring and management platform, the author proposes a research on smart grid asset management using digital twin technology and machine learning algorithms. This study achieves data security performance verification and monitoring through the application of data consensus mechanism. When verifying data in data nodes, data regulation verification information can be published in the form of a set of data packets to the data chain. The best node in the node is used as the data packet in the blockchain to build a platform structure. Management is achieved through data encryption, data monitoring, and device status classification. The experimental results show that the data theft rate simulated using this method has been reduced by 25.48%, and the security performance of the scheme is higher. The use of digital twin technology and machine learning algorithms for smart grid asset management has improved the security issues existing in traditional solutions and has enormous potential for application.