Information Quality Aware Data Collection for Adaptive Monitoring of Distribution Grids

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Abstract. Information from existing smart metering infrastructure, mainly used for billing purposes can also be utilised to monitor and control state of the grid. To add functionalities such as fault detection and real-time state estimation, data from smart meters should be accessed with increased frequency during run time. The data collection system should adapt to changing dynamics of the communication network and electrical grid. This paper first introduces adaptation functionalities for the data collection mechanism. To study and analyse the influence of configuration parameters that can be utilised for adaptation, a two-layer smart meter data access infrastructure is presented. An information quality metric, Mismatch Probability (mmPr) is introduced for the quantitative analysis of the two-layer data access system implemented in MATLAB based discrete event simulation study.
Original languageEnglish
Title of host publicationSGIoT : The 1st EAI International Conference on Smart Grid Assisted Internet of Things
PublisherEAI - European Alliance for Innovation
Publication date1 May 2017
ISBN (Electronic)978-1-63190-159-1
Publication statusPublished - 1 May 2017
EventThe 1st EAI International Conference on Smart Grid Assisted Internet of Things - Sault Ste. Marie, Canada
Duration: 11 Jul 201713 Jul 2017
http://sgiot.org/2017/show/home

Conference

ConferenceThe 1st EAI International Conference on Smart Grid Assisted Internet of Things
CountryCanada
CitySault Ste. Marie
Period11/07/201713/07/2017
Internet address

Fingerprint

Smart meters
Monitoring
Discrete event simulation
State estimation
Fault detection
MATLAB
Telecommunication networks
Chemical analysis

Keywords

  • Adaptive Data Collection, Network QOS, Smart Metering Infrastructures, Distribution Grid Monitoring, Information Quality

Cite this

Kemal, M. S., Olsen, R. L., & Schwefel, H-P. (2017). Information Quality Aware Data Collection for Adaptive Monitoring of Distribution Grids. In SGIoT: The 1st EAI International Conference on Smart Grid Assisted Internet of Things EAI - European Alliance for Innovation.
Kemal, Mohammed Seifu ; Olsen, Rasmus Løvenstein ; Schwefel, Hans-Peter. / Information Quality Aware Data Collection for Adaptive Monitoring of Distribution Grids. SGIoT: The 1st EAI International Conference on Smart Grid Assisted Internet of Things. EAI - European Alliance for Innovation, 2017.
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Kemal, MS, Olsen, RL & Schwefel, H-P 2017, Information Quality Aware Data Collection for Adaptive Monitoring of Distribution Grids. in SGIoT: The 1st EAI International Conference on Smart Grid Assisted Internet of Things. EAI - European Alliance for Innovation, The 1st EAI International Conference on Smart Grid Assisted Internet of Things, Sault Ste. Marie, Canada, 11/07/2017.

Information Quality Aware Data Collection for Adaptive Monitoring of Distribution Grids. / Kemal, Mohammed Seifu; Olsen, Rasmus Løvenstein; Schwefel, Hans-Peter.

SGIoT: The 1st EAI International Conference on Smart Grid Assisted Internet of Things. EAI - European Alliance for Innovation, 2017.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Information Quality Aware Data Collection for Adaptive Monitoring of Distribution Grids

AU - Kemal, Mohammed Seifu

AU - Olsen, Rasmus Løvenstein

AU - Schwefel, Hans-Peter

PY - 2017/5/1

Y1 - 2017/5/1

N2 - Abstract. Information from existing smart metering infrastructure, mainly used for billing purposes can also be utilised to monitor and control state of the grid. To add functionalities such as fault detection and real-time state estimation, data from smart meters should be accessed with increased frequency during run time. The data collection system should adapt to changing dynamics of the communication network and electrical grid. This paper first introduces adaptation functionalities for the data collection mechanism. To study and analyse the influence of configuration parameters that can be utilised for adaptation, a two-layer smart meter data access infrastructure is presented. An information quality metric, Mismatch Probability (mmPr) is introduced for the quantitative analysis of the two-layer data access system implemented in MATLAB based discrete event simulation study.

AB - Abstract. Information from existing smart metering infrastructure, mainly used for billing purposes can also be utilised to monitor and control state of the grid. To add functionalities such as fault detection and real-time state estimation, data from smart meters should be accessed with increased frequency during run time. The data collection system should adapt to changing dynamics of the communication network and electrical grid. This paper first introduces adaptation functionalities for the data collection mechanism. To study and analyse the influence of configuration parameters that can be utilised for adaptation, a two-layer smart meter data access infrastructure is presented. An information quality metric, Mismatch Probability (mmPr) is introduced for the quantitative analysis of the two-layer data access system implemented in MATLAB based discrete event simulation study.

KW - Adaptive Data Collection, Network QOS, Smart Metering Infrastructures, Distribution Grid Monitoring, Information Quality

UR - http://eudl.eu/proceedings/SGIoT/2017?order_year=asc

M3 - Article in proceeding

BT - SGIoT

PB - EAI - European Alliance for Innovation

ER -

Kemal MS, Olsen RL, Schwefel H-P. Information Quality Aware Data Collection for Adaptive Monitoring of Distribution Grids. In SGIoT: The 1st EAI International Conference on Smart Grid Assisted Internet of Things. EAI - European Alliance for Innovation. 2017