Instrumentation & Measurement Magazine 24-2 - 101

Application of Machine Learning
in Outdoor Insulators Condition
Monitoring and Diagnostics
Ayman El-Hag

P

ower 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.

Outdoor Insulator Diagnostics
In both distribution and transmission systems, outdoor insulators first support overhead lines to withstand different
mechanical loads, resulting from tensile, vibration, and compression forces under steady state conditions. Additional
mechanical loads from transient conditions such as earthquakes, wind and ice shielding are also expected. Secondly,
when the insulators electrically isolate the bare conductors
from the surrounding structures, they need to withstand the
steady state electric stress as well as the transient stresses resulting from lightning and switching events. Both the electrical

and mechanical roles of outdoor insulators are performed in
varying environmental conditions, which include temperature variations, moisture, pollution and ice accumulation and
ultra-violet radiation from sunlight.
There are two types of outdoor insulators used in both distribution and transmission overhead lines, namely: ceramic
and non-ceramic (polymer, composite) insulators. Each type
has its own pros and cons that are summarized in Table 1. As
depicted, each insulator type suffers from different problems
that need to be understood and monitored using regular inspection. Insulator inspection relies heavily on the technicians
and engineers experience and their ability to understand and
interpret the measured 1D (i.e., leakage current) or 2D (regular
photos) signals. With the scarcity of experienced outdoor insulators personnel, application of ML can help utilities to better
diagnose the health conditions of their outdoor insulators.
Nevertheless, in certain cases, assessing the insulator conditions may be even difficult for experienced personnel.
The objective of this article is to summarize the process of
ML application in the context of outdoor insulator diagnostics from capturing the signal until a decision is made. Also,
some examples in the literature of applying ML to assess the
conditions of both ceramic and non-ceramic insulators will
be discussed, especially those that are related to the Canadian
power grid. Examples of shortcomings of the existing literature will be briefly discussed. Finally, the future potential of
ML applications in assessing the health conditions of outdoor
insulators will be highlighted.

Table 1 - Comparison between ceramic and non-ceramic insulators
Ceramic
Advantages
Long history of use
Stable Material

April 2021	

Non-Ceramic
Disadvantages
Heavy weight;
Poor contamination
performance

Advantages
Light weight;
Good contamination performance;
Vandalism resistant;
High mechanical strength to weight
ratio

Disadvantages
Long-term behavior is relatively
unknown;
Susceptibility to aging

IEEE Instrumentation & Measurement Magazine	101
1094-6969/21/$25.00©2021IEEE



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