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Validation of Distributed Topology Inference of Distribution Networks usingthe IEEE Reliability Test System
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0002-9563-254X
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Reliability of power system depends on the up to date knowledge of the system state for operation and control. Shifting from large conventional production units to small and/or renewable DG connected in the distribution network means more control and monitoring system require for the Distributed System operator caused by active generation and reactive power consumption by DG. Therefore it is interesting to explore concepts in fast and scalable topology processors for monitoring and controlling applications such as state estimation, OPF and static and dynamic stability assessment in electrical distribution network the need is evident to validate with meshed network to analyze the overall performance of the proposed methodology\Decentralized Topology Inference of Electrical Distribution Networks". The topology inference processor is require minimal prior knowledge of electrical network structure by taking a series of time-stamped process measurements from each bays of each substation in the network and distinguished between connected and unconnected bays. This master thesis project has implemented an IEEE reference electric power distribution network in Simulink platform , integrating the reference electrical network with the Java-based multi agent topology inference application as well as having investigated. This project has included work in the real time simulation of a standard IEEE reference distribution network, OPC server interfacing between reference model and the topology inference application, testing and analysis of the application. The reference model is selected to provide a sucient case to analyses and validate the methodology.

Place, publisher, year, edition, pages
2014. , 85 p.
EES Examensarbete / Master Thesis, XR-EE-ICS 2014:002
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-143944OAI: diva2:709827
Educational program
Master of Science - Electric Power Engineering
Available from: 2014-04-15 Created: 2014-04-03 Last updated: 2014-04-15Bibliographically approved

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