Integration of Process Model and CIM to Represent Events and Chronology in Power System Processes

Ravikumar, Gelli; Khaparde, S.A.; Joshi, Rushikesh K.

doi:10.1109/jsyst.2016.2535719

Cited by 10 publications

(7 citation statements)

References 26 publications

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“…Literature Review MBSE, which supports complex systems engineering and development efforts [5] by formalizing development processes, system architectures, and operational interrelationships, has been widely applied in different industrial sectors. There are currently several modeling languages in use (e.g., SysML [6], object process methodology [7], and BPMN [8]), which provide modeling tools to describe real-world artifacts and processes using graphical views. Recently, researchers have proposed an Object Management Group standard for model-driven engineering, comprising a four-layer architecture [9].…”

Section: Methodsmentioning

confidence: 99%

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Zheng

et al. 2022

IEEE Systems Journal

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Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. Therefore, this article presents an ontology based upon graphs, objects, points, properties, roles, and relationships with extensions (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages. In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, MetaGraph, for evaluating its availability. The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.

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Section: Methodsmentioning

confidence: 99%

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Zheng

et al. 2022

IEEE Systems Journal

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“…To detect security intrusion or attacks of the power Internet of Things and cloud (IoT-Cloud), Choi et al [40] devised an ontology-based security context reasoning method to improve the security service. To orchestrate the utility business processes, Ravikumar et al [41] developed the CIM-based process ontology to model end-to-end process operations of power utilities. In renewable energy storage systems, Maffei et al [42] added an ontology subsystem to achieve the unified data formatting and provided semantics to the middleware.…”

Section: Ontology Learningmentioning

confidence: 99%

“…To orchestrate the utility business processes, Ravikumar et al. [41] developed the CIM‐based process ontology to model end‐to‐end process operations of power utilities. In renewable energy storage systems, Maffei et al.…”

Section: Research Status Of Electric Power Knowledge Miningmentioning

confidence: 99%

A survey on the development status and application prospects of knowledge graph in smart grids

Jian

Wang

et al. 2020

IET Generation Trans & Dist

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With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

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“…Ravikumar et al [47] proposed an extended model that integrates the process ontology model with CIM to represent utility business process as a standard process model for solution of interoperability problem between different systems. The extended model defines a PowerSystemProcess class and a Process artifact, which represents a whole process and a part of the process, respectively, similar to the association between the EquipmentContainer class and the Equipment class in CIM.…”

Section: Business Processmentioning

confidence: 99%

A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids

et al. 2020

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Smart grids with interoperability improve grid reliability by collecting system information and transferring it to an energy management system and associated applications through a seamless end-to-end connection. To achieve interoperability, it is required to exchange the semantic information within the different domains. The international electrotechnical commission has established the Common Information Model (CIM) tool, which is a standard application programming interface for the exchange of semantic information in power systems. CIM provides a robust framework for accurate data sharing, merging, and transformation into reusable information. However, as CIM provides a basic framework for information exchange, various practical issues arise in establishing an energy management system capable of exchanging information using CIM. This paper aims to offer a comprehensive understanding by summarizing and categorizing the research on the practical use of CIM for interoperability in smart grids. Many papers are analyzed and the issues are classified into CIM extension, harmonization, and validation to address the issues that arise when establishing an integrated information exchange system.

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Integration of Process Model and CIM to Represent Events and Chronology in Power System Processes

Cited by 10 publications

References 26 publications

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Design Ontology Supporting Model-Based Systems Engineering Formalisms

A survey on the development status and application prospects of knowledge graph in smart grids

A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids

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