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Zhang, Y., Zhang, C., Li, H., Chen, Q., Cheng, C. and Guo, P. (2022) An Online Detection Method for Capacitor Voltage Transformer Based on Load Classification. 2022 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD), Harbin, 30 November-2 December 2022, 1-6.
https://doi.org/10.1109/icsmd57530.2022.10058214

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.