SEKE 2012 Proceedings - Knowledge Systems Institute

Fig. 1 an d the data elements in Fig. 2 ar e considered when
creating and training the predictive models.
A. Fault Prediction Models
Four models are created to identify weather patterns that are
most likely to result in a fault event using the NN, KSVM,
RPART, and NB algorithms. The models were constructed by
taking weather data points joined to fault events, as well as
random weather data s amples when no fault events were
recorded in the selected IOU substations. Since there were a
large number of weather points for the times at which a fault
did not occur, the random sample of records is taken making
sure that all months and days and hours in the day are covered
in the sample. The dataset contained a t otal of 3471 records
(1725 with faults and 1746 without faults) of which 2430 were
used for training each of the four models and 1041 for testing
the models (see Fig. 3). The selection of records without faults
was done in a random fashion. The best-performing model is
the one created with the feed-forward trained by a multi-layer
perceptron back-propagation NN algorithm (see shaded area in
Fig. 3). This trained model produced an accuracy of 75%, an
average precision of 77%, an average recall of 73%, and an f-
measure of 75%. Table I shows a sample of the results
generated in Rapid Miner for the NN model.
B. Zone Prediction Models
The four zone prediction models were trained by
considering historical fault data from the IOU grid and weather
data. Of the 1725 records with faults and weather data, 70%
were used for training and 30% for testing the trained models.
The output of these models predict in what zone (AMZ, UMZ,
PMZ) on the IOU grid the fault occurred. The best-performing
model was the one created training a Neural Network
algorithm, as shown in the shaded area in Fig. 3. The model
contains one hidden layer with 20 nodes. The model produced
an accuracy of 66%, an average precision of 69%, an average
recall of 68%, and an f-measure of 68%.
C. Substation Prediction Models
The four substation prediction models were trained by
considering historical fault data from the IOU grid and weather
data. Of the 1725 records with faults and weather data, 70%
were used for training and 30% for testing the trained models.
The output of these models predicts the IOU substation ID
where the fault occurred. The best performing model was the
one created with the RPART algorithm, as shown in the shaded
area in Fig. 3. This trained model produced an accuracy of
59%, an average precision of 66%, an average recall of 54%,
and an f-measure of 59%.
D. Infrastructure Prediction Models
The four infrastructure prediction models were trained by
considering historical fault data from the IOU grid and weather
data. Of the 1725 records with faults and weather data, 70%
were used for training and 30% for testing the trained models.
The output of these models predicts the type of infrastructure,
OH (overhead) or UG (underground) on the section of the IOU
grid where the fault occurred. The best-performing model was
the one created training a Neural Network algorithm, as shown
in the shaded area in Fig. 3. The model produced an accuracy
of 77%, an average precision of 62%, an average recall of 52%,
and an f-measure of 57%.
Weather_Lightning_
Fault_No_Fault
Table
TABLE I.
Fault
Prediction
Models
No Faults
Predicted
Models
NN
KSVM
RPART
NB
Faults
Predicted
Models
NN
KSVM
RPART
NB
Historical
substation data
table joined to
the weather and
other properties
Zone
Prediction
Models
Substation
Prediction
Models
Infrastructure
Prediction
Models
Feeder
Prediction
Models
Figure 3. Machine Learning Models
Weather, Lightning,
Infrastructure, date, time
table (only faults)
Zone
Predicted
Substation
Predicted
Infrastructure
Predicted
Feeder
Predicted
Models
NN
KSVM
RPART
NB
Models
NN
KSVM
RPART
NB
Models
NN
KSVM
RPART
NB
Models
NN
KSVM
RPART
NB
NEURAL NETWORK OUTPUT OF FAULT PREDICTION MODEL
E. Feeder Prediction Models
The four feeder prediction models were trained by
considering historical fault data from the IOU grid and weather
data. Of the 1725 records with faults and weather data, 70%
were used for training and 30% for testing the trained models.
The output of these models predicts the IOU Feeder where the
fault occurred. The best-performing model was the one created
with the recursive partitioning algorithm, as shown in the
shaded area in Fig. 3. This trained model produced an accuracy
of 74%, an average precision of 79%, an average recall of 70%,
and an f -measure of 74%.
F. Comparative f-measures of Predictive Models
Fig. 4 displays a graph with the average f-measure values
from the four different models created for each analysis. The f-
measure is calculated as the harmonic mean of precision and
recall, using the formula given in Eq. 1. Precision pertains to
the fraction of classified set of data points that have been
correctly classified. Precision can be viewed also as the
probability that a (randomly selected, using the uniform
distribution) retrieved data point is relevant. Recall is the
fraction of the actual set of data points that have been correctly
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