  As society becomes increasingly reliant on electricity, the reliability
requirements for electricity supply continue to rise. In response,
transmission/distribution system operators (T/DSOs) must improve their networks
and operational practices to reduce the number of interruptions and enhance
their fault localization, isolation, and supply restoration processes to
minimize fault duration. This paper proposes a machine learning based fault
prediction method that aims to predict incipient faults, allowing T/DSOs to
take action before the fault occurs and prevent customer outages.
