<![CDATA[Partial discharge (PD) measurement as one of well-known method to evaluate the condition of high voltage (HV) power cables has been studied over many decades. Cable insulation failure could result in a power outage, which could then cause a loss of service in the transportation system and even dangerous events like fire accidents. It is of a great interest to railway infrastructure operators to monitor and identify the cable faults before any possible accident occurs. The paper focuses on the diagnostic problem to detect the HV cable fault based on the Phase Resolved Partial Discharge (PRPD) patterns. Classification models, such as Random Forest and Convolutional Neural Network, are considered to classify the pattern of PRPD based on the mostly occurring PD types in HV cables, such as corona, surface, and void patterns. Experiments are performed and the PRPD data from the experiments are collected. The optimal model is applied in the online monitoring program which will be used continuously to evaluate the cable condition and arrange the optimal schedule for maintenance. According to the analysis, both algorithm perform well in the PRPD pattern categorization, with accuracy up to 83.45%. This indicates that due to the more effective behavior, PD assessment with PD sensors is preferable.]]>
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