Singh, R.P., Sonawane, A.V., Satpute, M.S., Shirsath, D.Y. and Thakre, M.P. (2020) A Review on Traditional Methods of Condition Monitoring of Transformer. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, 2-4 July 2020, 1144-1152.
https://doi.org/10.1109/icesc48915.2020.9155858

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.