A Fast Vacuum Arc Detection Method Based on the Neural Network Data Fusion Algorithm for the High-Voltage DC Power Supply of Vacuum Tubes

Vacuum arc is one of the most important failure factors of the vacuum tubes. The amount of delivered energy from the high-voltage dc power supply to the vacuum tube is an important issue during the vacuum arc in the tube. Vacuum arc acts as a short-circuit fault (SCF) at the power supply output. The majority of converters use a single current sensor to measure only the converter output current for detecting the SCF. However, the sensor may provide unreliable data because of the noise effect. Application of a low-pass filter reduces the noise effect. Regarding the delay of the low-pass filter, the interval of arc detection increases and more energy is delivered to the tube. In this article, a fast vacuum arc diagnosis system is proposed based on the neural network data fusion algorithm. The proposed scheme consists of two sensors: the conventional current sensor and the existing voltage sensor used for controlling and monitoring. The data of these two sensors are combined by neural networks to diagnose the vacuum arc and to reject false alarms in noisy environments. The proposed method has a fast response time to the real SCFs while it remains inactive against the false data which are generated by noise. Simulations and experimental tests are carried out to evaluate the proposed scheme.