SEKE 2012 Proceedings - Knowledge Systems Institute

The investigation described in this paper focuses on
forecasting fault events in a distribution grid utilizing historical
data on fault events that occurred in the grid and their
associated electrical values, data on weather conditions at the
time of t he fault, and the type of grid infrastructure, as shown
in the shaded areas in Fig. 1. Although large storms can cause
considerable damage to distribution grids, “relatively normal”
weather conditions can also have a significant impact on faults
in the distribution grid, as shown by the results of this project.
Lu et al. [11] discuss the influence that changing climatic
conditions and weather have on the wear of electric equipment.
Their analyses indicate that during the hot summer months,
when the load on a feeders increase to 60-70%, there is an
over-charge in the lines and assets, which can result in
increased number of faults and reduced voltage of the
distributed power.
III. DATA COLLECTION AND PRE-PROCESSING
The results described in this investigation are associated
with the study of a utility in the US whose identity and name
cannot be revealed due to confidentiality agreements. Hence,
the utility in this study will be referred to as Investor Owned
Utility (IOU).
Several types of historical datasets associated with the IOU
were collected and utilized during this investigation. The
historical dataset types utilized in this investigation include: (a)
fault data and electrical values from the IOU; (b) weather data;
and (c) infrastructure type of the IOU. Fig. 2 shows the dataset
types and their associated data attributes. The fault data from
the IOU was collected utilizing an automated system developed
at ABB. This system consists of in telligent electronic devices
(IED’s) with sensing and analytic capabilities located at the end
of the feeders of distribution lines. These IED’s monitor
electrical values from the distribution lines, and are able to
detect a fault event in the grid soon after it occurs. The
historical fault data utilized includes these electrical values,
which were corroborated with data entries documented by IOU
engineers after restoring service. The weather data was
collected from the US National Weather Service (NWS) and
from the WeatherBug (WBUG) weather services. The NWS
data was collected by their weather station every five minutes
in METAR format. The WeatherBug data were collected from
small weather stations located in various locations close to the
different substations of the IOU. Finally, the lightning data
were also obtained from the WeatherBug weather services
organization. Fig. 1 has three gray rectangles that show the
factors utilized to develop the machine learning algorithms
discussed in this paper. It is e xpected that future studies will
include data associated with the remaining factors in Fig. 1, to
increase the precision and accuracy of the forecasted results.
An essential aspect of any data mining activity is preparing
the data to be util ized for the analyses, or "data preprocessing".
A software utility was developed in this project to
automate the pre-processing of the raw datasets, to generate the
data warehouse used to extract data for analyses, and to
populate the forecasted results. The data pre-processing utility
contains rules that address: (a) weather-fault time zone
alignments; (b) weather-fault distances; (c) weather direction;
and (d) information in free-format comments in the fault
events, among others. Fig. 2 shows a d etailed list of the data
attributes utilized to run the prediction models presented in
Section V.
Figure 2. Primary data attributes utilized by machine learning algorithms
Data pre-processing involved cleaning of the fault, weather,
and lightning data; aligning all of the datasets based on both
time and geographic location; and fusing the various datasets
together. A total of 1725 fault events were obtained over a twoyear
period, across eight feeders in four substations (two
feeders per substation). These feeders range from a few miles
apart to 10 miles apart. The main tasks in cleaning and preprocessing
fault data included generation of “mineable”
parameters from the fault comment texts, which described the
equipment involved in the fault and the type of problem (e.g.
animal contact). The fault infrastructure coding in the fault
events was supplemented with information on the power grid
topology, e.g. which feeders were almost entirely underground
(UG) or overhead (OH). Five-minute weather observation data
were obtained from NWS for one airport within 9-25 miles of
the four substations, with 93% completeness (about 100,000
observation records per year). Hourly weather observation data
were obtained from WeatherBug for four local “Earth
Networks” weather stations within varying distances from the
four substations at IOU (about 8000 observation records per
year for a single weather station).
Preprocessing the five-minute airport weather data required
several transformation steps: first, to translate the coded
“METAR” strings to their equivalent text and numeric
parameters; then, to parse and group the weather conditions
into mineable nominal parameters. The hourly WeatherBug
data contained somewhat different parameters than the fiveminute
METAR data (e.g., it included sunlight level readings
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