Electric Power ›› 2024, Vol. 57 ›› Issue (11): 62-69.DOI: 10.11930/j.issn.1004-9649.202307020

• Power System • Previous Articles     Next Articles

State Estimation and Bad Data Detection in Hybrid AC/DC Systems with LCC/MMC

Huashi ZHAO1(), Yaohui HUANG2, Zhiqiang SONG2, Jianzhong XU2(), Kexin ZHENG2, Kangkang LIANG2   

  1. 1. Power Dispatching Control Center of CSG, Guangzhou 510670, China
    2. State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
  • Received:2023-07-06 Accepted:2023-10-04 Online:2024-11-23 Published:2024-11-28
  • Supported by:
    This work is supported by the Science and Technology Project of CSG (No.ZDKJXM20200052).

Abstract:

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 and LCC, MMC, and between LCC and MMC, the unified iterative method is used to model the AC/DC state estimation of the 500kV subnetwork. Subsequently, the 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.

Key words: CIM/XML, AC/DC state estimation, LCC, MMC, detection and identification of bad data, maximum likelihood residual test