The converter valve is the core equipment of DC transmission projects

and its value accounts for about 22%-25% of the total price of converter station equipment. Its operating status directly affects the reliability of the DC transmission system. This paper proposes a fault location method for the valve area of a converter station based on metric learning and knowledge reasoning

which addresses the problems of low efficiency

long time consumption

and heavy dependence on the level of operating personnel in manual fault location. A valve area fault simulation model is constructed based on power system computer aided design (PSCAD)

generating typical fault waveform data for the valve area. The top 100 maximum spectral lines are obtained through time-frequency domain transformation. A data dimensionality reduction network based on metric learning is constructed to achieve feature extraction of recorded electrical quantities by maximizing inter class distance and minimizing intra class distance. A triplet of electrical quantity-switch quantity-fault type is formed

which stores fault location knowledge in the form of a knowledge graph

and designs a knowledge graph inference algorithm to achieve fault location based on waveform data analysis. The experimental results show that the proposed method achieves a fault location detection rate of over 92% based on the recorded waveform data of the converter station valve area

which will effectively improve the efficiency of fault location for operators.