Application of Machine Learning in Outdoor Insulators Condition Monitoring and Diagnostics

Power grid failure is very costly to any modern society, and preventing upheavals like the blackout in eastern US and Canada in the summer of 2003 is extremely important. Complete power grid failure may be triggered by the failure of a transformer, underground cable, overhead line insulator or any other component of the power grid. While close monitoring of expensive, centrally located assets like transformers, generators and circuit breakers is feasible and economically justified, it is extremely difficult to continuously monitor assets that are spread over long distances, and in some cases very difficult to reach, like overhead lines accessories and outdoor insulators. Condition monitoring of outdoor insulators is prohibitively costly, time consuming and unsafe. To overcome these problems, the use of machine learning (ML) in outdoor insulators condition monitoring and diagnostics could be a viable solution.