Instrumentation & Measurement Magazine 24-2 - 104

like predicting the actual value of the leakage current. On the
other hand, in the classification problem the objective is to predict a class rather than a specific value, for example, classes like
excellent, good and bad or high, medium and low. The classification problem can stem from what was originally a regression
problem, for example, rather than predicting a specific value
like equivalent salt deposit density (ESDD), instead predicting
the ESDD class as severe, medium or light. Also, certain problems are by nature classification problems like classifying the
silicone rubber surfaced hydrophobicity level using the Swedish transmission research institute (STRI) guide to one of seven
different classes.
Different classifiers have been used in the literature in the
context of outdoor insulators' condition monitoring and diagnostics. Examples of these classifiers include artificial neural
networks (ANN), support vector machines (SVM), k-nearest neighbors (KNN), adaptive neuro fuzzy inference systems
(ANFIS) and polynomial classifiers. Experience using these
classifiers revealed that there is no single classifier that consistently outperforms all other classifiers, and hence, application
of ML is a combination of art and science.
The application of ML usually starts with splitting the data
into training and testing sets, as depicted in Fig. 1. Once the
model is well trained, the testing dataset will be used to assess the ML model using different metrics. Examples of these
metrics are correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) for regression
problems and precision (P), recall (R) and accuracy rate for
classification problem.

Evaluating Outdoor Insulators using
Machine Learning
Examples of outdoor insulators evaluation using ML will be
highlighted in the following subsections. The focus will be on
two aspects of outdoor insulator problems: physical damage
and pollution.

Detection of Physical Damage in Outdoor
Insulators

It has been reported that millions of aged ceramic insulators are still in operation in North America including the
Canadian power system grid [4]. Hence, it is crucial to detect
physical damages before they trigger full failure of the insulator that may lead to power system outage. One of the most
commonly used methods is the buzz-test method which involves checking each insulator in the string by successively
applying a high voltage across it. If the insulator is faulty, then
a short circuit will occur that might present a safety concern,
which along with other disadvantages has led some utilities
like Hydro Quebec to seek other methods for faulty porcelain
insulator detection [5].
These defects may initiate partial discharge activities that
will result in the emission of electromagnetic waves or ultrasound signals that can be detected by RF antenna and acoustic
sensors, respectively. Moreover, both the RF antenna and the
acoustic sensors can also detect other forms of discharge in the
form of corona or surface discharge due to hardware defects
or pollution accumulation, respectively. Hence, there is a need
not only to detect these defects but also to classify them which
can be achieved using ML (as will be explained next).
Two different studies have been conducted at the University of Waterloo's high voltage lab to detect and identify
physical defects in ceramic insulators using RF antenna [6]
and acoustic sensors [7] along with ANN such as the ML algorithm. The details of the sensor specification, selected features,
detected problems and classification accuracy are summarized
in Table 3 and Fig. 4. Both the RF antenna and acoustic sensors
achieved reasonably high classification accuracy with relatively better accuracy of the RF antenna.
The relatively higher accuracy using the RF method could
be due to the fact that, unlike the RF antenna that captured the
full RF signal, the acoustic sensor only captured the envelope
of the acoustic signal and hence, may have lost some important information. Nevertheless, while the RF antenna showed
better results than the acoustic method, the acoustic method
has the advantage that it can be used to detect dry band arcing
that resulted from surface pollution. This is due to the fact that
dry band arcing only emits a low frequency magnetic field that
cannot be detected by RF antenna.
Detection of damages in non-ceramic insulators' surface
starts by detecting the change in the insulator surface from
hydrophobic to hydrophilic. There have been several efforts
to automate the hydrophobicity classification process using

Both ceramic and non-ceramic insulators suffer from aging
that may lead to physical damage and eventually to complete
failure. The nature and consequence of the physical damage in
ceramic insulators is totally different than the physical damage
in non-ceramic insulators. While the main forms of physical
damage in ceramic insulators are cracks or internal
voids, in non-ceramic insulators erosion of the
polymeric material (mainly
silicone rubber) is the main
severe form of physical
damage. Examples of both
erosion in a silicone rubber insulator and a surface
crack in a ceramic insulator
are depicted in Fig. 3.
Fig. 3. Different forms of physical damage in non-ceramic insulators (erosion) and ceramic insulators (surfaced crack).
104	

IEEE Instrumentation & Measurement Magazine	

April 2021



Instrumentation & Measurement Magazine 24-2

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