Resilient Cyber-Physical Energy Systems Using Prior Information Based on Gaussian Process

The power grid infrastructure is a large-scale, heterogeneous, and complex cyber-physical system, which forms the lifeline of modern societies. trend tight coupling physics, communication, computation in energy systems (CPESs) evident by inclusion numerous measurement sensors. This contributes to enhancing monitoring control functionalities CPESs. At same time, occurrence adverse effects constitutes vital dimension CPES operation. Increasing resilience critical key importance for safeguarding national economy security. article considers problem optimal estimation with sensing measurements subject arbitrary corruption resulting from effects. Such signals can cause false situation awareness and/or trigger sequence cascading leading an ultimate system failure. We formulate as constrained optimization additional prior information posed set constraint on vector. It shown that if satisfies certain conditions, recovery error bound improved. approach demonstrates enhancement resiliency using Gaussian process basis generative probabilistic regression model historical data. validation mechanism performed New York Independent System Operator data, demonstrating 100% successful state up 60% sensor failures.