A Brief History: The Common Information Model [In My View]

Wollenberg, B.F.; Britton, Jay; Dobrowolski, Ed; Podmore, Robin; Resek, Jim; Scheidt, John; Russell, J. M.; Saxton, Terry; Ivanov, Chavdar

doi:10.1109/mpe.2015.2481787

Cited by 5 publications

(2 citation statements)

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“…Special focus is on the integration of Renewable Energy Sources (RES), which expands distributed power generation, and the Internal Energy Market (IEM) to meet the EUs energy policy objectives of affordability, sustainability and security of supply. This will invariably increase the data exchange necessities both internal and between the European utility enterprises and hence the relevance and need of a common semantic framework like CIM [13].…”

Section: Cim Modelingmentioning

confidence: 99%

Visualization Techniques for Electrical Grid Smart Metering Data: A Survey

Stefan

Lopez²,

Andreasen³

et al. 2017

2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)

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One of the considerable initiatives towards creating a smart society could be the guarantee of a smart, resilient and reliable power grid. As an attempt to improve the electricity supply service, it would be meaningful for the distributed system operators (DSOs) to be able to monitor the current status of the grid. The prediction of future possible critical situations would then be feasible using the available information, whereas, based on historical data, further grid expansion and reinforcement may be planned. A proper presentation and visualization of the near-real time metering data may constitute the baseline for bringing improvements to the power grid. This paper presents an approach to build an efficient visualization system so that the extracted smart meters information can be used in a meaningful manner. An overview of the use cases related to the visualization features is first presented, as a motivation for the choice of the relevant state of the art research. In relation to the knowledge provided by the metering data, a definition of the big data concept will be further introduced, according to the requirements established by the project definition. Geographic Information System (GIS) tools are useful to help visualize the collected big data in near-real time. For this reason, a survey of existing GIS software will be made so that the choice of the most suitable tool can be justified. Also, the integration of GIS technologies into the Common Information Model (CIM) aims to improve the visualization efficiency. As a consequence, investigating methods for adapting CIM standards to the GIS platform are also important.

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Section: Cim Modelingmentioning

confidence: 99%

Visualization Techniques for Electrical Grid Smart Metering Data: A Survey

Stefan

Lopez²,

Andreasen³

et al. 2017

2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)

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“…To ensure such interoperability, industrial standards are core to many industries and represent vital elements in the ecosystem of that industrial domain by providing shared guidelines for data representation and exchange. For example, the International Electrotechnical Commission's (IEC) Common Information Model (CIM) provides a data model for standardized information exchange of energy grid descriptions [42]. Another example is OPC Unified Architecture (OPC UA, https://opcfoundation.org/) -an industrial standard that ensures interoperability between industrial devices on the technical, syntactical, and semantic levels: while the transport protocols and payload formats are defined on the technical and syntactical levels, semantic interoperability is achieved through extensible information models, which capture domain-specific knowledge.…”

Section: Introductionmentioning

confidence: 99%

Deriving semantic validation rules from industrial standards: An OPC UA study

Bareedu,

Frühwirth,

Niedermeier

et al. 2024

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Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards in terms of their compliance with the modeling constraints prescribed by the guidelines. This raises the risk of costly interoperability errors induced by the incorrect use of the standards. There is, therefore, an increased interest in automatic semantic validation of information objects based on industrial standards. In this paper we focus on an approach to semantic validation by formally representing the modeling constraints from unstructured documents as explicit, machine-actionable rules (to be then used for semantic validation) and (semi-)automatically extracting such rules from pdf documents. While our approach aims to be generically applicable, we exemplify an adaptation of the approach in the concrete context of the OPC UA industrial standard, given its large-scale adoption among important industrial stakeholders and the OPC UA internal efforts towards semantic validation. We conclude that (i) it is feasible to represent modeling constraints from the standard specifications as rules, which can be organized in a taxonomy and represented using Semantic Web technologies such as OWL and SPARQL; (ii) we could automatically identify modeling constraints in the specification documents by inspecting the tables ( P = 87 %) and text of these documents (F1 up to 94%); (iii) the translation of the modeling constraints into formal rules could be fully automated when constraints were extracted from tables and required a Human-in-the-loop approach for constraints extracted from text.

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