Making Distribution Automation Work: The Need for Smart Data
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Figure 3 Relationships between key elements of an asset management system (from ISO 55002). and incorporate multiple analytics packages. The focus of ARMS 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 work and asset management system (WAMS). There is a tremendous amount of asset-related data available within the enterprise that can feed into a strategic asset-management program. Some examples are: SCADA Historian Geospatial Information System (GIS) Intelligent Electronic Devices (IEDs) Work and Asset Management Systems (WAMS) that have inspection and work order data Laboratory Information Management System (LIMS) Databases that store results of diagnostic tests It is possible also 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. Also, 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. 
Figure 4 Realizing the value in data through asset analytics. In order to get a complete picture of the asset and fleet condition, 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 that describes 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 asset decision and support applications—including strategy definition and prioritization, maintenance strategy planning, and risk management—with the aim of maximizing value. Typical information exchanges for such applications include: Asset data from enterprise data sources to ARMS Asset list and characteristics provided by EAP to ARMS Asset location data provided by GIS to ARMS Asset measurements and test results provided by historian and test databases to ARMS Asset inspection and maintenance data provided by WAMS to ARMS Actionable intelligence from ARMS to various enterprise systems Risk-ranked asset list provided by ARMS to EAP for replacement planning Asset health and condition assessments provided to GIS for provision to the field force Work request provided to 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 Simple Object Access Protocol (SOAP) over Hypertext Transfer Protocol (HTTP), where the information exchange is defined in an Extensible Markup Language Schema Definition (XSD). A specific realization of ARMS focused on substation assets may comprise the following elements: Online condition monitoring devices for power transformers, circuit breakers, and batteries stream data to a historian. Diagnostic test results and inspection results are stored in database systems. An analytic suite continuously assesses the data to determine asset condition. For instance, in the case of power transformer: Among the analysis would be the monitoring of dissolved gases in the transformer oil for the presence of any fault condition indicators. If the assessment predicts a health event—such as a high risk of bubble formation—an actionable intelligence alert is provided to designated systems and/or persons. An analytic suite periodically assesses and helps optimize the asset use. The various relevant data systems for asset management are owned by different entities within the utility enterprise. Due to organizational and security barriers, it is typically very challenging to bring together the 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 that are needed for a business use case or process of interest, the utilities can implement role-based access control (RBAC) or attribute-based access control (ABAC) 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. Directory: Projects -> IEEEmagCIM -> Shared%20Documents -> Article%204%20-%20Making%20distribution%20automation%20work%20-%20the%20need%20for%20smart%20data -> Drafts Projects -> Terminal Decision Support Tool Systems Engineering Graduate Capstone Course Aiman Al Gingihy Danielle Murray Sara Ataya Projects -> Rajinder Sachar Committee Projects -> Cape Lookout National Seashore Historic Resource Study By Projects -> Cape Lookout National Seashore Historic Resource Study By Projects -> Revolutionizing Climate Modeling – Project Athena: a multi-Institutional, International Collaboration Projects -> What is a Hurricane? Projects -> General Information Projects -> Press Information Projects -> 1 Introduction 3 2 Designing an Embedded System 4 Drafts -> Making Distribution Automation Work: The Need for Smart Data Download 1.36 Mb. Share with your friends:
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