A novel measurement error identification framework for multi-branch CT system | Zendy

Bolun Du | Zendy; Yinglong Diao | Zendy; Huan Wang | Zendy; Xiujuan Zeng | Zendy; Tong Liu | Zendy; Yiyi Peng | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A novel measurement error identification framework for multi-branch CT system

Author(s) -

Bolun Du,

Yinglong Diao,

Huan Wang,

Xiujuan Zeng,

Tong Liu,

Yiyi Peng

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3590166

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The current transformer (CT) is recognized as a critical power conversion and metering device in substations, with its operational stability being considered crucial for grid reliability. A measurement error identification framework for error threshold-exceeding in multi-branch CT systems is proposed in the study. First, the secondary-side currents of each phase in multi-branch CTs are decomposed using robust empirical mode decomposition (REMD), with residuals being extracted to characterize current trend variations. The current residuals are then mapped into principal component space and residual space through kernel principal component analysis (KPCA), where the squared prediction error (SPE) in residual space is calculated as the error characteristic.The total SPE is generated as the summation of phase SPEs through accumulation of all branch CTs. During normal multi-branch CT operation, the total SPE is employed to establish error monitoring thresholds via adaptive kernel density estimation (AKDE) at 99% confidence level. When real-time total SPE surpasses the predetermined threshold, threshold-exceeding CT measurement errors are detected within the multi-channel CT system. Subsequently, the contribution rate of each branch CT’s relative threshold exceedance is calculated. The CT phase demonstrating the highest contribution rate is identified as the tolerance threshold-violating component. The proposed threshold-exceeding identification methodology has been validated through multiple experimental cases at the China Electric Power Research Institute. In all test scenarios, threshold-exceeding conditions were accurately detected within multi-branch CT systems, with both the non-compliant CTs and their specific phases being precisely identified.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research