A Robust Short-Circuit Fault Diagnosis for High Voltage DC Power Supply Based on Multisensor Data Fusion

Short-circuit fault (SCF) detection is mandatory in a high voltage DC power supply (HVPS) to prevent fatal damage. The majority of converters employ a single sensor to detect the SCF. This attribute increases the interference vulnerability of the fault detection (FD) system in the presence of noise. Therefore, miss detections and false alarms are possible to occur. Miss detections and false alarms are harmful catastrophes in most applications. A commonly used method to suppress the noise impacts is using a low-bandwidth low-pass filter. However, the use of the low-bandwidth low-pass filter reduces the speed of FD due to the filter delay. This paper proposes a fast FD algorithm based on multisensor data fusion. The proposed FD system consists of three FD subsystems with high-bandwidth low-pass filters, which each one is individually implemented by sensing three distinct variables and reaches a local decision about the SCF occurrence. Finally, all local decisions are gathered in a decision fusion center and a global decision regarding the converter situation will be made based on the weighted decision fusion. The simulation and experimental results confirm the performance of the proposed FD system in the presence of noise.