Abnormal state recognition method for power-heatgas integrated system based on unified power flow model

Abstract

Tight coupling between different energy systems makes state analysis of multi-energy integrated systems difficult. From the perspective of data-driven, this paper proposes an anomaly state identification method for power-heat-gas integrated system based on unified power flow model. First, establish a unified power flow model of the power-heat-gas integrated system. Then, using the random matrix theory in big data technology, the historical data and real-time data of the state quantity in the unified power flow model form a random matrix. Finally, the M-P law and the ring law in the random matrix theory are used to qualitatively analyze the system operation. The simulation results verify that the method does not need to identify the physical structure of the system, which provides a new idea for the abnormal state recognition of the power-heat-gas integrated system.