Leveraging capital projects for GIS success
Based on a 1997 GIS implementation planning study, OUC knew that one of the data
conversion needs was to collect more accurate facility locations and information about
the facilities. The cost for converting the data was included in the GIS implementation
plan. At the time, the cost exceeded the amount OUC was able to budget. Subsequently,
the project was postponed.
However, over the last three years, OUC has used two capital projects to develop
components of the GIS database. OUC’s outage management system implementation and
field data collection project was used to build essential database components for the
implementation of GIS. The approaches taken significantly reduced the cost for data
migration. The projects were designed to meet the future GIS database model
requirements.
The first project was the implementation of an Outage Management System. To support
the OMS a complete recompilation of the feeder/circuit maps to include full connectivity
of all devices and conductors. This also produced a set of circuit drawings that met the
topological requirements of the GIS. The second project which I will describe more fully
was a full electric structure field inventory.
The field data collection project was driven by the need for a current and accurate street
light and joint use inventory. This task could only be accomplished by field inventorying
each pole. We also added to the project scope, the collection of all pad mounted
transformers and switchgear.
To improve tracking and maintenance of the facilities an asset number (unique number)
was attached to each structure. Because there was a need to improve the positional
accuracy of facility structures and validate information about the system, the project was
expanded to include the collection of GPS data and device information in addition to
foreign attachments and street lights. The cost to collect the additional information
was minimal. The majority of the cost for the field inventory was the time spent getting
to each structure.
To assure the project outcome would be compliant with the GIS, a default electric
database model was used to define the field data dictionary. The goal was to limit the
amount of translation work necessary during the data conversion phase of a future GIS
implementation.
A Microsoft Access database was developed containing tables and attributes replicating
the electric database model. The database included only features that were to be
collected as part of the field data collection project. This effort facilitated the migration
of the collected into OUC’s electric GIS database with minimal translation work.
Another major benefit is the reduction of work necessary to adjust the electric facilities to
the more accurate landbase being provided by the Property Appraiser. The collection of
an accurate geographic location eliminated the need for the conversion technicians to
move each structure, conductor and device to a more accurate location. Instead, a
program was developed to link the device and the structure and snap the conductor and
devices to the new accurate location.
This was accomplished by assigning the structure asset number to the devices on that
structure in the digital source drawings (this was a normal map maintenance activity).
The task then was to confirm the snapping programs solution and make any corrections
because of errors in the original data. The time savings from this automated process
directly translated into lower conversion costs.
Not only was the cost for data conversion moved from the GIS implementation budget
but the outcome of the field data collection project also produced increased revenue. The
additional revenue further reduced the cost of the GIS implementation.
In closing, OUC gained several advantages toward their eventual GIS implementation by
performing the field data collection project and the OMS implementation prior to, and as
independent capital projects not associated with the implementation of GIS.
For one, conversion data quality was improved greatly. Secondly, time to convert data
was reduced by 50%. And thirdly, overall data conversion costs for the GIS
implementation were reduced by 50%.
