Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids

Mohammed Seifu Kemal, Rasmus Løvenstein Olsen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceabstrakt i proceedingForskning

Resumé

Smart Grid systems not only transport electric energy but also information which will be active part of the electricity supply system. This has led to the introduction of intelligent components on all layers of the electrical grid in power generation, transmission, distribution and consumption units. For electric distribution systems, Information from Smart Meters can be utilized to monitor and control the state of the grid. Hence, it is indeed inherent that data from Smart Meters should be collected in a resilient, reliable, secure and timely manner fulfilling all the communication requirements and standards. This paper presents a proposal for smart data collection mechanisms to monitor electrical grids with adaptive smart metering infrastructures. A general overview of a platform is given for testing, evaluating and implementing mechanisms to adapt Smart Meter data aggregation. Three main aspects of adaptiveness of the system are studied, adaptiveness to smart metering application needs, adaptiveness to changing communication network dynamics and adaptiveness to security attacks. Execution of tests will be conducted in real field experimental set-up and in an advanced hardware in the loop test-bed with power and communication co-simulation for validation purposes.
OriginalsprogEngelsk
TitelFast Abstracts and Student Forum Proceedings : EDCC 2016 - 12th European Dependable Computing Conference
RedaktørerHans Peter Schwefel, Gilles Tredan
Antal sider5
Vol/bind12
Publikationsdato5 sep. 2016
Udgave1
StatusUdgivet - 5 sep. 2016
Begivenhed12th European Dependable Computing Conference - Gothenburg, Sweden, Gothenburg, Sverige
Varighed: 5 sep. 20149 sep. 2016
http://edcc2016.eu/

Konference

Konference12th European Dependable Computing Conference
LokationGothenburg, Sweden
LandSverige
ByGothenburg
Periode05/09/201409/09/2016
Internetadresse

Fingerprint

Smart meters
Monitoring
Communication
Electric power systems
Electric power distribution
Telecommunication networks
Power generation
Information systems
Agglomeration
Electricity
Hardware
Testing

Citer dette

Kemal, M. S., & Olsen, R. L. (2016). Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids. I H. Peter Schwefel, & G. Tredan (red.), Fast Abstracts and Student Forum Proceedings: EDCC 2016 - 12th European Dependable Computing Conference (1 udg., Bind 12)
Kemal, Mohammed Seifu ; Olsen, Rasmus Løvenstein. / Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids. Fast Abstracts and Student Forum Proceedings: EDCC 2016 - 12th European Dependable Computing Conference. red. / Hans Peter Schwefel ; Gilles Tredan. Bind 12 1. udg. 2016.
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title = "Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids",
abstract = "Smart Grid systems not only transport electric energy but also information which will be active part of the electricity supply system. This has led to the introduction of intelligent components on all layers of the electrical grid in power generation, transmission, distribution and consumption units. For electric distribution systems, Information from Smart Meters can be utilized to monitor and control the state of the grid. Hence, it is indeed inherent that data from Smart Meters should be collected in a resilient, reliable, secure and timely manner fulfilling all the communication requirements and standards. This paper presents a proposal for smart data collection mechanisms to monitor electrical grids with adaptive smart metering infrastructures. A general overview of a platform is given for testing, evaluating and implementing mechanisms to adapt Smart Meter data aggregation. Three main aspects of adaptiveness of the system are studied, adaptiveness to smart metering application needs, adaptiveness to changing communication network dynamics and adaptiveness to security attacks. Execution of tests will be conducted in real field experimental set-up and in an advanced hardware in the loop test-bed with power and communication co-simulation for validation purposes.",
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Kemal, MS & Olsen, RL 2016, Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids. i H Peter Schwefel & G Tredan (red), Fast Abstracts and Student Forum Proceedings: EDCC 2016 - 12th European Dependable Computing Conference. 1 udg, bind 12, Gothenburg, Sverige, 05/09/2014.

Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids. / Kemal, Mohammed Seifu; Olsen, Rasmus Løvenstein.

Fast Abstracts and Student Forum Proceedings: EDCC 2016 - 12th European Dependable Computing Conference. red. / Hans Peter Schwefel; Gilles Tredan. Bind 12 1. udg. 2016.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceabstrakt i proceedingForskning

TY - ABST

T1 - Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids

AU - Kemal, Mohammed Seifu

AU - Olsen, Rasmus Løvenstein

PY - 2016/9/5

Y1 - 2016/9/5

N2 - Smart Grid systems not only transport electric energy but also information which will be active part of the electricity supply system. This has led to the introduction of intelligent components on all layers of the electrical grid in power generation, transmission, distribution and consumption units. For electric distribution systems, Information from Smart Meters can be utilized to monitor and control the state of the grid. Hence, it is indeed inherent that data from Smart Meters should be collected in a resilient, reliable, secure and timely manner fulfilling all the communication requirements and standards. This paper presents a proposal for smart data collection mechanisms to monitor electrical grids with adaptive smart metering infrastructures. A general overview of a platform is given for testing, evaluating and implementing mechanisms to adapt Smart Meter data aggregation. Three main aspects of adaptiveness of the system are studied, adaptiveness to smart metering application needs, adaptiveness to changing communication network dynamics and adaptiveness to security attacks. Execution of tests will be conducted in real field experimental set-up and in an advanced hardware in the loop test-bed with power and communication co-simulation for validation purposes.

AB - Smart Grid systems not only transport electric energy but also information which will be active part of the electricity supply system. This has led to the introduction of intelligent components on all layers of the electrical grid in power generation, transmission, distribution and consumption units. For electric distribution systems, Information from Smart Meters can be utilized to monitor and control the state of the grid. Hence, it is indeed inherent that data from Smart Meters should be collected in a resilient, reliable, secure and timely manner fulfilling all the communication requirements and standards. This paper presents a proposal for smart data collection mechanisms to monitor electrical grids with adaptive smart metering infrastructures. A general overview of a platform is given for testing, evaluating and implementing mechanisms to adapt Smart Meter data aggregation. Three main aspects of adaptiveness of the system are studied, adaptiveness to smart metering application needs, adaptiveness to changing communication network dynamics and adaptiveness to security attacks. Execution of tests will be conducted in real field experimental set-up and in an advanced hardware in the loop test-bed with power and communication co-simulation for validation purposes.

UR - https://arxiv.org/html/1609.01055

M3 - Conference abstract in proceeding

VL - 12

BT - Fast Abstracts and Student Forum Proceedings

A2 - Peter Schwefel, Hans

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ER -

Kemal MS, Olsen RL. Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids. I Peter Schwefel H, Tredan G, red., Fast Abstracts and Student Forum Proceedings: EDCC 2016 - 12th European Dependable Computing Conference. 1 udg. Bind 12. 2016