IEEE Power & Energy Magazine - January/February 2016 - 62

is a domain that spans the utility enterprise, stretching from the
customer-premise meters to substation assets to lines and poles
and involving disparate functional areas of planning and procurement, maintenance, and operation. Consequently, the assetmanagement decisions that play out on a daily basis are varied
and complex and are, therefore, best made on the basis of a
dispassionate analysis of relevant data. this domain provides a
good example of how utilities are realizing the value in data and
the enabling role of the Cim in this endeavor.
high-value assets such as power transformers are routinely kept in service beyond their nominal lifetime. from
a planning and procurement perspective, analysis of repairversus-replace decisions for such assets is driven by riskmanagement calculations. from a maintenance perspective,
right-sizing the maintenance program requires an accurate
assessment of the asset condition, which is made on the basis
of asset health analytics. from an operational perspective,
smart tradeoff of use-related deterioration against revenue
is a key aspect of maximizing distribution system efficiency.
these are but some considerations in what constitutes a multidimensional problem. there is growing realization of the
value of tackling such complex problems strategically. relevant standards for these are the British standard pas-55
and the international standard iso 55000.
as shown in figure 3, iso 55000 specifies asset management that 1) is iterative and arises from the organizational objectives, 2) is embodied in a strategic asset-management plan,
and 3) incorporates performance evaluation so that its ongoing
contributions to the organizational objectives can be affirmed.
this enables the institutionalization of best practices in asset
management and provides for consistent outcomes. the crux of
this approach is analytics that, on the basis of all available data
pertinent to the assets, compute health scores, rank the assets,
and provide actionable intelligence. there is a variety of such
analytic solutions being deployed in utilities, ranging from those
that focus on a particular asset class to comprehensive asset
risk-management systems (armss) that span multiple asset
classes and incorporate multiple analytics packages. the focus
of armss on optimally and sustainably managing assets-
their performance, risks, and expenditures-differs from and
complements the procurement-planning and work-centric viewpoints of an enterprise asset-planning (eap) system and a workand-asset-management system (wams).
there is a tremendous amount of asset-related data available within the enterprise that can feed into a strategic assetmanagement program. some examples are
✔ sCaDa historian
✔ geospatial information system (gis)
✔ intelligent electronic devices
✔ wams with inspection and work order data
✔ laboratory information management systems
✔ databases that store results of diagnostic tests.
it is also possible that some of these datasets are held in
ad hoc forms and are not in persistent storage systems. for
instance, test results could be in spreadsheets or flat files.
62

ieee power & energy magazine

additionally, data from an online condition monitor may be
held in the device itself, and only the alerts from the device are
manually checked. in such cases, it makes sense to put in place
data persistence systems to extract value from the data. with
the maturation of cloud-based technologies and the strengthening of security measures around them, it may make sense to
consider cloud-based systems for such ad hoc datasets.
to get a complete picture of the asset and fleet condition, all this distributed data needs to be gathered and
analyzed. the Cim is the logical choice as the semantic
model for this task-in particular, the ieC 61968-4 standard describing the Cim-based messages that enable integration of disparate asset-related data for arms analytics.
a new edition of this standard is planned in 2016 to support applications such as strategy definition and prioritization, maintenance strategy planning, and risk management.
typical information exchanges for such applications include
a number of elements:
✔ asset data from enterprise data sources to the arms
*	asset lists and characteristics provided by the eap
to the arms
*	asset location data provided by the gis to the arms
*	asset measurements and test results provided by the
historian and test databases to the arms
*	asset inspection and maintenance data provided by
the wams to the arms
✔ actionable intelligence from the arms to various
enterprise systems
*	risk-ranked asset list provided by the arms to the
eap for replacement planning
*	asset health and condition assessments provided to
the gis for provision to the field force
*	work requests provided to the wams on the basis of
deteriorating health indicators.
the use cases that these information exchanges are
intended to support include alerting upon meaningful change
in asset health for condition-based maintenance and automating changes to the gis, such as normal-open to normal-close
due to seasonal switching. the messages specified in 61968-4
essentially define the payloads for the exchanges and identify
the mandatory and optional elements of the payload. the interacting systems can exchange these payloads using one among
the various technologies specified in ieC 61968-100, such as
the simple object access protocol (soap) over hypertext
transfer protocol (http), where the information exchange is
defined in an Xml schema definition (XsD).
a specific realization of an arms focused on substation
assets may include the following elements:
✔ online condition-monitoring devices for power transformers, circuit breakers, and batteries that stream
data to a historian
✔ diagnostic test results and inspection results stored in
database systems
✔ an analytic suite that continuously assesses data to determine asset condition (e.g., power transformer analysis
january/february 2016



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

IEEE Power & Energy Magazine - January/February 2016 - Cover1
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IEEE Power & Energy Magazine - January/February 2016 - Cover3
IEEE Power & Energy Magazine - January/February 2016 - Cover4
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