SEKE 2012 Proceedings - Knowledge Systems Institute

which the airport data did not contain, and it did not have
weather condition comments), but the pre-processing was
otherwise similar. Lightning stroke data was also obtained for
2009 and 2010 from WeatherBug Total Lightning Network
(WTLN) within a five-mile radius of each substation. Since this
lightning stroke data were time-stamped to the micro-second,
preprocessing it for data mining purposes required various
aggregate counts and sums of strokes and amplitudes for both
intra-cloud and cloud-to-ground lightning. These aggregates
were calculated for both five-minute periods and one-hour
periods so the lightning counts could be joined to the weather
data.
To enable selection of the “closest” weather data for each
fault, preparing all o f this data for mining required aligning
both the timestamps and the geographic locations. Calculating
geographic location parameters was also dif ferent for each
dataset. For the airport weather data, the precise (lat, lon) of the
airport weather station was known. For the hourly weather
data, the (lat, lon) values were not available for the weather
stations, so the geographic center of each weather station’s zip
code was used. Since each microsecond-time-stamped
lightning stroke had its own unique (lat, lon), a reference (lat,
lon) was determined algorithmically to tag the five-minute and
one-hour aggregate records. The precise (lat, lon) had not been
recorded for many faults, so the (lat, lon) of the associated
substation was used for all fault records to ensure consistency.
Each fault and weather dataset had a different reference
time zone: some used local time and some used UTC, and
some that used local time reflected Daylight Savings while
others did not. Therefore, our preprocessing included
calculation of a new timestamp parameter for each dataset,
adjusted to the same reference time zone (local standard time
was chosen). The lightning aggregates were then joined to the
five-minute weather data and the hourly weather data.
Approximate ‘Great Circle’ distances were calculated between
each weather station and each substation, and for each lightning
aggregate, the distances to the four substations were calculated.
These distances were used to choose for each fault the “closest”
five-minute and “closest” one-hour weather record, using both
timestamp and distance.
IV. FORECASTING FAULT EVENTS
Several approaches to predicting fault events in a
distribution grid have been proposed. Butler [1] discusses a
failure detection system, which makes use of electrical property
parameters (such as feeder voltages, phase currents,
transformer windings' temperatures, and noise from the
transformer during its operation) to identify failures.
Gulachenski and Bsuner [6] use a Bayesian technique to
predict failures in transformers. Some of the features
considered in these studies include ambient temperature,
varying loads on the transformer, and age-to-failure data of
transformers. Quiroga et al. [12] search for fault patterns
assuming the existence of r elationships between events. Their
approach considers factors such as values of over-currents of
past fault data and the sequence, magnitude, and duration of the
voltage drops. Although the above mentioned approaches are
predictive in nature, they do not consider weather properties to
predict faults. The hypothesis associated with th e work
presented in this paper is that a fault event occurrence is likely
to follow a pattern with respect to weather conditions,
infrastructure type, and electrical values in the distribution grid
at the time of the fault (as seen in Fig. 1).
The objective of the investigation discussed in this paper is
to develop the basis of a Decision Support System (DSS)
using machine learning that forecasts fault events in the power
distribution grid based on expected weather conditions. The
strategy has two primary phases as shown in Figure 4. In the
first phase, the historical data collected (as explained in
Section 3) is utilized to d evelop and train machine learning
models that can foretell fault events using weather forecasts.
During the first phase, four algorithms are utilized to perform
five generic analyses (fault prediction, zone prediction,
substation prediction, infrastructure prediction, and feeder
prediction) and train the models. Four measurements are used
to compare machine learning algorithms: precision, recall,
accuracy, and f-measure. Precision determines the fraction of
records that actually turns out to be positive in the group the
classifier has declared as a positive class. Recall is computed as
the fraction of correct instances among all instances that
actually belong to the relevant subset. Accuracy is the degree
of closeness of the predicted outcomes to the actual ones. The
f-measure is the harmonic mean of precision and recall.
During the second phase, the best performing algorithm is
selected for each of the five analyses, to be uti lized as part of
the DSS for future predictions at the IOU.
V. CREATION OF MACHINE LEARNING MODELS
Supervised classification techniques are utilized to forecast
the occurrence of faults in the distribution power grid of the
IOU. Four supervised classification machine learning
algorithms are utilized to conduct the analyses: Neural
Networks (NN), kernel support vector machines (KSVM),
decision-tree based classification (recursive partitioning;
RPART), and Naïve Bayes (NB). A NN is an interconnected
group of artificial neurons that use a computational model that
allows them to adapt and change their structure based on
external or internal information that flows through the network.
A SVM is a linear binary classification algorithm [1]. Since the
datasets consist of more than two classes, we choose to use
kernel support vector machine (KSVM), which has been found
to work in the case of non-linear classifications. RPART is a
type of decision tree algorithm that helps identify interesting
patterns in data and represents them as a tree. RPART is chosen
because it provides a suitable tree-based representation of any
interesting patterns that ar e observed in the data sets, and also
because it works well with both nominal and continuous data.
NB is a probabilistic classification technique that works well
with relatively small datasets. These four algorithms are
selected because of their distinct properties and their ability to
work with different types and sizes of data, and the objective is
to select the algorithm that performs the best for the type of
prediction being conducted. Five analyses are conducted
utilizing these four algorithms: (a) fault event prediction; (b)
grid zone prediction; (c) substation prediction; (d) type of grid
infrastructure; (e) feeder number prediction. As mentioned
earlier, the primary data attributes shown in the shaded areas of
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