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

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

BookEnglish2021

Publisher, publication year, extent ...

KTH Royal Institute of Technology,2021
48 s.
electronicrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:kth-292323
ISBN:9789178738243
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292323URI

Supplementary language notes

Language:English
Summary in:English

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SwePubSwePub

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Subject category:vet swepub-contenttype
Subject category:lic swepub-publicationtype

Series

TRITA-EECS-AVL ;2021:22

Notes

QC 20210330
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 and genre

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

Added entries (persons, corporate bodies, meetings, titles ...)

Hilber, Patrik,Associate Professor,1975-KTH,Elektroteknisk teori och konstruktion(Swepub:kth)u1sm6ffu (thesis advisor)
Westerlund, PerKTH,Elektroteknisk teori och konstruktion(Swepub:kth)u197h0d7 (thesis advisor)
Shayesteh, EbrahimKTH,Elektroteknisk teori och konstruktion(Swepub:kth)u15kqkyy (thesis advisor)
Koutsopoulos, Iordanis,Associate ProfessorAthens University of Economics and Business, Athens, Greece (opponent)
KTHElektroteknisk teori och konstruktion (creator_code:org_t)

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