Distribution Network Fault Prediction Utilising Protection Relay Disturbance Recordings And Machine Learning

TL;DR

This paper introduces a machine learning approach for predicting faults in distribution networks using protection relay disturbance recordings, aiming to enable proactive maintenance and reduce outages.

Contribution

It presents a novel fault prediction method leveraging protection relay data and machine learning to improve fault detection and response in power distribution systems.

Findings

Effective fault prediction accuracy demonstrated

Reduced fault detection time achieved

Potential for proactive outage prevention

Abstract

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.