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Chen, H., Cai, J., Li, Y., Lin, X., Chen, Z., Chen, J., et al. (2023) Research on the Transmission Relationship of Grid Science and Technology Innovation Indicators under the Smart Grid. 2023 2nd International Conference on Smart Grids and Energy Systems (SGES), Guangzhou, 25-27 August 2023, 19-22.
https://doi.org/10.1109/sges59720.2023.10366922

has been cited by the following article:

• TITLE: Real-Time Error Analysis of Multi-Channel Capacitive Voltage Transformer Using Co-Prediction Matrix

  AUTHORS: Jiusong Hu, Ao Xiong, Yongqi Liu, Guaxuan Xiao, Yi Zhong

  KEYWORDS: Capacitive Voltage Transformers, Co-Prediction Matrix, High-Voltage, Measurement error

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.13 No.1, January 24, 2025

  ABSTRACT: Capacitive voltage transformers (CVTs) are essential in high-voltage systems. An accurate error assessment is crucial for precise energy metering. However, tracking real-time quantitative changes in capacitive voltage transformer errors, particularly minor variations in multi-channel setups, remains challenging. This paper proposes a method for online error tracking of multi-channel capacitive voltage transformers using a Co-Prediction Matrix. The approach leverages the strong correlation between in-phase channels, particularly the invariance of the signal proportions among them. By establishing a co-prediction matrix based on these proportional relationships, The influence of voltage changes on the primary measurements is mitigated. Analyzing the relationships between the co-prediction matrices over time allows for inferring true measurement errors. Experimental validation with real-world data confirms the effectiveness of the method, demonstrating its capability to continuously track capacitive voltage transformer measurement errors online with precision over extended durations.