A Vacuum Arc Diagnosis Method for the High Voltage Power Supply of Vacuum Tubes

Abstract

Vacuum tubes are widely used for various applications. These vacuum tubes are supplied by high voltage power supplies. The amount of delivered energy from the high voltage power supply to the vacuum tube is an important issue during the vacuum arc in the tube. The protection mechanism consists of a shunt crowbar which diverts the fault current from the tube to itself as a parallel path. Detection of the vacuum arc is crucial and only one sensor is usually employed to detect the vacuum arc. This characteristic intensifies the interference susceptibility of the vacuum arc diagnosis system in a noisy environment. As a result of the noise, the arc detection system can report false alarms. False alarms are very likely to damage to both the vacuum tube and the high voltage power supply. A low-pass filter is an usual preventive measure of reducing the noise effect. Decreasing the bandwidth of the filter leads to the reduction of noise effects, while the delay of the filter diminishes the speed of the vacuum arc detection system. The more interval of arc detection increases, the more energy is delivered to the tube, and the more damage the tube suffers during the arc fault. Accordingly, a fast and noise-robust vacuum arc detection scheme is crucial to protect the tube. In this paper, a fast vacuum arc diagnosis system is proposed based on neural networks. The proposed scheme consists of two sensors those their data are combined by neural networks to diagnose the vacuum arc and to reject false alarms in a noisy environment. In order to adjust the neural networks weights, Levenberg-Marquardt algorithm is used. Simulations tests are carried out to evaluate the proposed scheme.