interests range from motion control and electromechanical energy conversion to electric drives for applications in automotive, renewable energy, household appliances and aerospace sectors. He is a fellow of the IET and a senior member of IEEE.Kalhana Colombage received the M.Eng. degree in electronic engineering with employment experience from the University of Sheffield, Sheffield, U.K., in 2010, where he received the Ph.D. degree for his thesis "Design and control of on-board bidirectional battery chargers with islanding detection for electric vehicle applications" in 2015.

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DC filters are responsible for more than half of the failures in the power electronic converters. One approach to improving the reliability and maintainability of the converters is to include failure diagnosis for the DC filters within the power converter. DC filters failures may be classified as sudden faults which may take the form of breakdown faults resulting from a blown capacitor fuse and gradual faults caused by capacitor aging. This paper presents a fault detection and location for the capacitor aging faults in the DC filters of the power converters. The proposed fault diagnosis is based on the adaptive neuro-fuzzy inference system (ANFIS) algorithm. The inputs to the ANFIS unit are only the input voltage of the converter as well as the voltages across the DC filters. The output of the ANFIS unit is utilized as an index in order to identify the capacitor aging fault in the power converter. Then, it locates the fault within the two DC filters installed in the power converter.

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