Online Error Inversion-Based Assessment of the Internal Insulation Performance of a Capacitor Voltage Transformer

The internal insulation parameters of Capacitor Voltage Transformers (CVTs) are susceptible to the influence of environment and aging during operation, which leads to the degradation of its insulation performance and affects the safe and stable operation of power system. Since the current insulation testing method is essentially a periodic testing of power outage, which has the problems of difficulty of power outage and untimely testing, this paper proposes an online assessment method of CVT internal insulation performance based on error inversion. This method innovatively takes the measurement error of CVT as a characteristic parameter to characterize the insulation state of CVT, establishes a feature set of insulation state composed of specific and angular differences, and then uses Convolutional Neural Network and fuzzy analysis to form a data-knowledge double-layer-driven on-line assessment model of CVT’s internal insulation performance. The experimental results show that the proposed method has an accuracy of 99.76% for the identification of typical internal insulation states, and the average error of quantitative evaluation of internal insulation parameters is better than 3%, which effectively realizes the online evaluation of CVT internal insulation parameters.