On line component level soft fault diagnostics for power converters
The implementation of diagnostic methodologies to power devices and converters has become essential and critical in many fields. A lot of previous research demonstrated detection of hard faults, such as open or shorted switching elements in the power systems. However, little progress has been made on soft faults diagnosis. The soft faults related to components degradation degrade the system's performance but not completely affect the normal operation. A method of on line model based components soft faults diagnosis is proposed in this paper. The fault detection technique utilizes parameter identification method based on Particle Swarm Optimization algorithm to achieve the component level diagnostic purpose. Considering components degradation and variable operating conditions, several simulation experiments have been conducted to validate the presented approach. The results show that this technique characterizes high computation efficiency and good estimation accuracy. Also, it will be a basis of further study for the circuit level condition monitoring and remaining useful life prediction.
2016 Prognostics and System Health Management Conference (PHM Chengdu)