IEEE Power & Energy Magazine - January/February 2016 - 64

to monitor dissolved gases in transformer oil for fault
condition indicators; if a health event is predicted-
such as a high risk of bubble formation-the suite
provides actionable intelligence to designated systems
or persons)
✔ an analytic suite that periodically assesses and helps
optimize asset use.
the various relevant data systems for asset management
are owned by different entities within the utility enterprise, as
shown in figure 4. Due to organizational and security barriers,
it is typically very challenging to bring together these disparate
datasets. But there is substantial value in being able to access
the data together for analytical purposes. Because the Cim
standards clearly identify the data profiles needed for a business use case or process of interest, the utilities can implement
role-based access control or attribute-based access control
schemes to allow the access of only the data necessary for their
use cases. By sharing only the necessary data and no more, the
utilities can realize the value in their data while ensuring overall information security.

The CIM for a
Distribution Management System
over the last two decades, myriad vendor-dependent systems
and sCaDa systems have been part of utility automation,
which provides support for the system operators to make
prompt decisions based on the state of the grid at any given
time. the interoperation between many of the proprietary

Operational
Data

Online
Monitors

Inspection

systems is a challenging task for the electric power utilities.
the Cim for a distributed management system (Dms) (ieC
61968) has been established for developing seamless interapplication integration of a power utility, which conducts
various operational and business processes for efficiently
handling a distribution network.
implementation of the Cim by distribution utilities is seeing
greater adoption, and it continues to mature. figure 5 depicts an
implementation architecture for distribution utility application
integration using the Cim. the architecture is comprised of four
layers: databases, Cim data management, applications, and data
sources. there can be numerous data sources, such as real-time
measurements received from sCaDa, a network model received
from gis, or energy meter readings received from advanced
metering infrastructure (ami)/automatic meter reading. the
data is stored in a data warehouse and accessed by multiple utility applications through the Cim data layer.
the Cim data layer mainly includes adapters for converting data into Cim-compliant data definitions and for accessing data with databases. the software systems-namely the
Dms, energy-management system (ems), outage-management system (oms), distribution automation (Da), customer
data management (CDm), and ami-communicate with
the Cim message payloads over the enterprise service bus
(esB). the core Dms functions are listed in the table 1. for
all functions and subfunctions to be carried out at the Dms,
the Cim can provide a common database via a Cim-oriented
warehouse. the Dms interacts with smart-grid data and

Work Order

Test Results

Labs

Raw Data

Aggregate

Data Integration

Analytics 1
Analyze

Asset-RiskManagement
System (ARMS)

Analytics 2
Analytics N

Act

Actionable
Intelligence

Strategic AssetManagement
Plan

figure 4. Realizing the value in data through asset analytics.
64

ieee power & energy magazine

january/february 2016



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - January/February 2016

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IEEE Power & Energy Magazine - January/February 2016 - Cover3
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