SEKE 2012 Proceedings - Knowledge Systems Institute

Once all datasets were collected, all these data points were
aligned using the closest reading to the location and time stamp
at which the fault event occurred in a power line, to create an
analysis dataset. Moreover, a random selection of weather data
points with no faults was also included in the datasets utilized
to train and test the machine learning models. A total of 1725
faults occurred during the study period. The dataset then
consisted of fault-related data and weather corresponding to the
1725 fault events as well as 1746 weather entries when no
faults occurred.
The machine learning algorithms evaluated for creating the
prediction models include neural networks, kernel support
vector machines, recursive partitioning, and Naïve Bayes. The
f-measure was utilized to evaluate the overall performance of
the machine learning algorithms; the average f-measure value
for the selected prediction models was 67%. The models that
performed the best included neural networks and recursive
partitioning, which were selected to become the basis for th e
fault prediction decision support system (DSS). If we were to
pick only one machine learning algorithm that performed the
best across all five prediction areas, it is the neural network
algorithm. The authors believe this is because the inputs to the
model are well understood (we understood which data
attributes were likely to be important for prediction, but did not
know how to combine them). Moreover, the predicted data
attributes were clearly defined. Another reason was that we had
enough examples to be able to train the neural network
algorithms.
It is the authors’ expectation that including data such as
physical properties of the power grid (e.g. pole foundation
characteristics, materials of poles and cables, types of
connectors) to train the machine learning algorithms in the DSS
can result in better predictive models. Similarly, including
historical data on maintenance (such as rate o f cable breaks,
equipment replacement history, and specific maintenance
history) can also enhance the predictive capability of the
models. Also, including component degradation data such as
insulator integrity levels, cable zinc coating degradation,
overhead cable sagging, cable strand fatigue, age of assets, etc.,
can also positively contribute to better-performing predictive
algorithms.
Other areas of future work include extending this study in
multiple dimensions. First, adding more years of both fault and
weather data for these substations can improve the training of
the algorithms. Second, having more precise (lat, lon) locations
will enable a closer alignment of faults with weather data.
Third, including data from more substations and from utilities
which face substantially different climatic conditions can help
enhance the capabilities of the DSS algorithms.
It is the belief of the authors, based on the investigation
conducted and presented in this paper, that the use of machine
learning algorithms to help forecast fault events in the power
distribution grid has the po tential of reducing the ti me and
effort in restoring electrical power in the grid after a fault event
has occurred.
ACKNOWLEDGMENTS
The authors wish to recognize and thank the WeatherBug
organization for sharing a portion of the historical weather data
that was utilized for the analyses conducted in this work. The
positive results of th is project were enhanced by the data and
help provided by the WeatherBug weather services.
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