Situation awareness method using spectral analysis of random matrix for integrated energy system

Situation awareness is essential to ensure operation of integrated energy systems consisting of the electricity, gas and heat systems. However, the multi-energy flow characteristics of system result in strong coupling relationships among different subsystems including different detection variables, which bring new challenges to situation awareness. To address this issue, a data driven detection method based on spectral analysis of random matrix is proposed in this paper. Firstly, a detection matrix model, which combines different types of variables, is established to fully reflect the interdependencies among subsystems, both internal and external. Furthermore, a novel detection method, which analyzes the degree of the spectral deviation of presented model, is presented to accomplish situation awareness. The proposed method can effectively handle the problem of power-gas-heat coupling, multi-variable modeling and rapid situation judging without requiring complicated numerical model. With this effort, not only the changed time but also the position of changed node could be obtained simultaneously through only spectral computation. Finally, simulation results are presented to illustrate the effectiveness of the proposed detection method.