DGetting the Most Out of Data: The Key to Successful Electrical Utility Application Integration
What are the types of data errors that can impact an OSS and where do the errors come
from? Data errors can result from data migration and data integration efforts if these
projects are not properly monitored and controlled. Here, incompatible data formats,
incomplete data, and duplicate data from two or more sources can offer stiff project
challenges above and beyond what may have been inaccurate to start with. However,
most errors result from a lack of proper data-entry business processes and data
ownership. Typically, these errors arise due to limited error checking at various stages in
the data management process. Another contributing factor is the inability of personnel
who rely on the data to easily make corrections. Other error sources are often beyond the
control of the utility. For example, when performing restoration efforts after a major
storm (ice, hurricane, etc.), the primary focus is to get power back to customers, not to
accurately report how the network was altered. Data-capture problems during these
emergencies are compounded by the use of “foreign” crews who are not familiar with the
local utility’s data-capture procedures.
The following represents the situation encountered after performing an initial assessment
on a typical sample data set:
| 1 |
Anchor Text Outside of File |
(39 of 7,992) |
| 2. |
Linear Objects (Primaries, Secondaries, Services) with 1 vertex |
(1,582 of 26,682) |
| 3. |
Construction Spans w/o phasing 30 missing phasing 102 missing phasing, node ids, and circuit ids |
(132 of 3,174) |
| 4. |
Fuses/Switches/Reclosers w/o conductors 102 not aligned w/ conductor or Open/Close status unset |
(348 of 348) |
| 5. |
Primary Conductors node id unset |
(6 of 20,513) |
| 6. |
Network connectivity issues 75 logical connectivity (node id attribute) problem 57 graphical connectivity problem |
(132 of 3,193) |
On a percentage basis, these problems may not seem extreme, but their on various
systems can be very significant.
The connectivity and phasing problems discovered would have severe impact on the
operations of an OMS or an engineering analysis package. Inaccurate data can create a
multitude of problems—wrong assumptions about which device has operated to clear a
fault, sending crews to the wrong location, over or under designed facilities, and false
facility loading predictions that can lead to shortened facility life and potentially
dangerous situations.
Other errors are associated with switching updates, loads, construction types, conductor
sizes, consistency in device information like regulator settings, and customer-to-facility
(usually the transformer) linkage. These data errors can also pose problems for a variety
of applications. So, the question that remains is how do we detect and fix these errors?
