Logic-Based Methods for Intelligent Fault Diagnosis and Recovery in Power Electronics

This paper presents generalized logic-based methods for intelligent fault diagnosis in power electronic converters based on correlation between faults and basic measurements. Fault recovery is then applied based on this correlation by using necessary signals and quantities from existing measurements. The main purpose of the proposed fault diagnosis methods is for power electronic systems to survive from fault conditions that could occur in various components, to cope with the notion of a smart grid and extend their lifetime. The proposed methods are online, i.e., real-time or near-real-time, and can be applied to any power electronic system. Existing intelligent control of power electronic systems along with various short- and open-circuit faults in major power electronic components are reviewed. Two methods are established to diagnose faults and engage redundancy for fault recovery with one method using combinational logic and another using fuzzy logic. In both methods, two quantities are observed for each of the measured signals: 1) the signal's average value and 2) the signal's RMS value. Total harmonic distortion is also used in some simulations but is not required in experimental implementation. A systematic methodology to reduce the number of measured quantities while maintaining effective diagnosis is introduced. A solar PV microinverter in standalone mode is used as an example testing platform for the proposed methods. A simulation model is experimentally validated and the effect of each fault on different voltage and current measurements are observed, then both methods are tested in simulation and hardware. Results show the ability of both methods to diagnose several faults in the inverter's power stage along with their ability to engage redundancy for fault recovery.