In　this　article,　the　fault　diagnosis　problem　for　power　converters　is　considered.　Given　that　the　existing　fault　diagnosis　models　rarely　address　the　problems　of　the　data　noise　and　the　new　faults　that　are　never　emerged　in　the　database,　thus,　an　optimized　fault　diagnosis　model　for　power　converters　based　on　temporal　convolutional　network　(TCN)　is　proposed.　Our　contributions　include　the　following:　1)　unknown　faults　can　be　efficiently　distinguished　with　an　optimized　classifier;　2)　the　proposed　model　has　good　robustness　and　reliability　under　noisy　environment　without　any　subsidiary　predenoising　algorithm;　and　3)　it　can　realize　adaptive　feature　extraction,　and　the　parameters　are　small.　Experimental　results　on　a　three-phase　voltage　inverter　platform　demonstrate　that　the　proposed　approach　is　efficient　and　can　be　adaptively　applied　to　various　real　applications.