Detection and identification of bad data in AC/DC hybrid systems with LCC and MMC

Based on the CIM/XML and CIM/E documents exported from the regional dispatching system, this paper focuses on data generation and starts by converting the exported documents into raw input data for state estimation. Considering the interactions between the AC system, LCC, MMC, and LCC-MMC interfaces, a unified iterative method is proposed to model the state estimation of the 500 kV subnetwork. Subsequently, Gaussian noise is added to the original measurement data, and the maximum residual test method is employed for detecting and identifying bad data. Finally, the effectiveness of the proposed models for AC/DC state estimation and the detection and identification of bad data are validated through simulation data.