Review on Monitoring Technology of CVT Internal Insulation State Based on Secondary Voltage Signals

Firstly, this paper analyses the working principle of Capacitor Voltage Transformer (CVT) and the characteristics of inner insulation state, and explains the reason why the inner insulation state of CVT is suitable for state detection by analysing the influencing factors of the inner insulation state of CVT. On this basis, several internal insulation detection schemes widely used at present, including infrared measurement method, signal analysis method and group correlation detection method, are briefly introduced their implementation principles, advantages and disadvantages and scene limitations. However, in the actual engineering application, the internal insulation state evaluation of capacitive voltage transformer often needs to be carried out by multi-dimensional and multi-factor state evaluation. Considering that the connection between the secondary data analysis method of capacitors and machine learning algorithm can make up for the online monitoring in actual working conditions, the paper further introduces the principle of using the Hilbert-Yellow analytical signal and extracting the feature signal by deep neural network algorithm to realize the online insulation detection in CVT.