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Automated Model Transformation for Cyber-Physical Power System Models
KTH, School of Engineering Sciences (SCI).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Standardized information and mathematical models, which model the characteristics of the power generation and power transmission systems, are requirements for future development and maintenance of different applications to operate the electrical grid. Available databases such as Nordpool provides large amounts of data for power supply and demand [1]. The typical misconception with open availability of data is that existing power system software tools can interact and process this data. Difficulties occur mainly because of two reasons. The first on is the amount of data produced. When the topology of the electrical grid changes e.g. when a switch opens or closes, the flow of electrical power changes. This event produce changes in generation, transmission and distribution of the energy and different data sets are produced. The second problem is the representation of information [2]. There are a limited number of software tools that can analyze this data, but each software tool requires a specific data format structure to run. Dealing with these difficulties requires an effective way to transform the provided data representation into new data structures that can be used in different execution platforms. This project aims to create a generic Model-to-Text (M2T) transformation capable of transforming standardized power system information models into input files executable by the Power System Analysis Tool (PSAT). During this project, a working M2T transformation was never achieved. However, missing functionality in some programs connected to sub processes resulted in unexpected problems. This led to a new task of updating the information model interpreter PyCIM. This task is partially completed and can load basic power system information models.

Place, publisher, year, edition, pages
2017. , 6 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-219449OAI: oai:DiVA.org:kth-219449DiVA: diva2:1163171
Examiners
Available from: 2017-12-06 Created: 2017-12-06 Last updated: 2017-12-06Bibliographically approved

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  • apa
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  • Other locale
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Output format
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