From data collection to electric grid performance: How can data analytics support asset management decisions for an efficient transition toward smart grids?

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From data collection to electric grid performance : How can data analytics support asset management decisions for an efficient transition toward smart grids?

Koziel, Sylvie Evelyne, 1985- (author): KTH,Elektroteknisk teori och konstruktion,QEDAM

Hilber, Patrik, Associate Professor, 1975- (thesis advisor): KTH,Elektroteknisk teori och konstruktion

Westerlund, Per (thesis advisor): KTH,Elektroteknisk teori och konstruktion

Shayesteh, Ebrahim (thesis advisor): KTH,Elektroteknisk teori och konstruktion

Koutsopoulos, Iordanis, Associate Professor (opponent): Athens University of Economics and Business, Athens, Greece

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(creator_code:org_t)

ISBN 9789178738243
KTH Royal Institute of Technology, 2021
English 48 s.
Series: TRITA-EECS-AVL ; 2021:22

Related links:: https://kth-se.zoom....; show more...; https://kth.diva-por... (primary) (Raw object); https://urn.kb.se/re...; show less...

Licentiate thesis (other academic/artistic)

Abstract Subject headings

Physical asset management in the electric power sector encompasses the scheduling of the maintenance and replacement of grid components, as well as decisions about investments in new components. Data plays a crucial role in these decisions. The importance of data is increasing with the transformation of the power system and its evolution toward smart grids. This thesis deals with questions related to data management as a way to improve the performance of asset management decisions. Data management is defined as the collection, processing, and storage of data. Here, the focus is on the collection and processing of data.First, the influence of data on the decisions related to assets is explored. In particular, the impacts of data quality on the replacement time of a generic component (a line for example) are quantified using a scenario approach, and failure modeling. In fact, decisions based on data of poor quality are most likely not optimal. In this case, faulty data related to the age of the component leads to a non-optimal scheduling of component replacement. The corresponding costs are calculated for different levels of data quality. A framework has been developed to evaluate the amount of investment needed into data quality improvement, and its profitability.Then, the ways to use available data efficiently are investigated. Especially, the possibility to use machine learning algorithms on real-world datasets is examined. New approaches are developed to use only available data for component ranking and failure prediction, which are two important concepts often used to prioritize components and schedule maintenance and replacement.A large part of the scientific literature assumes that the future of smart grids lies in big data collection, and in developing algorithms to process huge amounts of data. On the contrary, this work contributes to show how automatization and machine learning techniques can actually be used to reduce the need to collect huge amount of data, by using the available data more efficiently. One major challenge is the trade-offs needed between precision of modeling results, and costs of data management.

Subject headings

TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)

Keyword

asset management
data analytics
data management
distribution system operators
electrical power grid
machine learning
real-world datasets
Electrical Engineering
Elektro- och systemteknik

Publication and Content Type

vet (subject category)
lic (subject category)

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