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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. [ABSTRACT FROM AUTHOR]

This paper presents a combinational-logic-based approach for identifying faults that could occur in the drivetrain of an electric vehicle (EV). A real-time simulation model of an EV is used to study the behavior of available sensor signals and control commands, with specific measured quantities, during vehicle operation. Those quantities could be, but are not limited to, mean values of the phase currents, DC bus current, traction motor speed (if available), in addition to control command quantities. Focus is given to these quantities which are abundant in vehicle inverters and controllers for no added sensing costs. Those quantities carry information of offsets and disturbances that could occur under faulty operating conditions compared to nominal operation, and are thus observed and studied under these conditions. Using such information, a method is developed using simple combinational logic and thresholds to diagnose a fault occurring at any time during a vehicle drive cycle. By combining features of measured quantities that behave similarly irrespective of when the fault occurs during a driving cycle, the proposed method is fault-time-insensitive. The proposed method is presented and validated by real-time simulations to capture over 20 different faults injected at different drive cycle times and in different drivetrain components—electric machine, inverter, transmission, and sensors. Results show that the proposed method is able to robustly and successfully diagnose different faults irrespective of when would they occur. [ABSTRACT FROM AUTHOR]

This article proposes an unsupervised learning approach for fault prognosis of silicon carbide (SiC) mosfets. The proposed approach utilizes the changing trend of a device's voltage, current, temperature, and other device characteristics with its degradation. The failure modes of semiconductors are reviewed along with existing methods for fault prognosis. The proposed approach is the first to address prognostics of SiC devices, and it can avoid the effects from system noise and data errors. It is not limited to offline analysis and is targeted at online implementation. It is easy to implement on standard digital platforms, and has fast computational speed. Offline data analysis is performed to verify the effectiveness of the proposed method, and a processor-in-the-loop system is used to verify its ability to perform online fault prognosis. [ABSTRACT FROM AUTHOR]