Optimal asset management data - Components

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Project abstract

Distribution grids are experiencing changes because of the integration of new loads, such as electric vehicles, and distributed generation such as solar panels. This represents a challenge to ensure the reliability of power supply. Proposed solutions often include the development of a cyber-physical system, where sensors generate data that enable operators to model the grid, or even to create a ”digital twin”. The processing of the collected data provides information that supports efficient decision taking, to keep the grid reliable, even with a high penetration of renewables and electric vehicles.

However, the installation of sensors and meters on a large scale might encounter three major issues: i) Economic issue: It might be unprofitable to install many sensors, compared to the benefits they provide. ii) Environmental issue: The collection, processing and storage of very large amounts of data can be power-intensive. ii) Technical issue: Adding equipment and functionality always carries a risk of giving rise to new failure modes as well as new uncertainties.

The objective of the PhD is to reflect on the value of data, and to adapt the ICT system accordingly, so that the physical grid evolves towards a smarter grid. The first step has been to establish the relations between data and grid performance. Then, two approaches have been identified: Approach 1: Use data already collected more efficiently and Approach 2: Explore how much additional data are needed to achieve a reliable smart grid.

Summary of work

2018.
Conception of the framework for the project: improvement of asset management in the power systems based on data analytics.
Selection of three main areas to investigate: i) prediction, ii) selection, iii) detection.

2019.
Work on the impacts of data quality on asset management decisions, and selection of an profitable level of investments in improved data quality.
Work on failure prediction at the component level using machine learning methods (research visit in Japan).

2020.
Literature review of algorithms that can be used for detection purposes in local power systems.
Work on failure prediction at the substation level.
Work on the elaboration of importance indices for components at the distribution level to support maintenance activities.

2021.
One journal paper written and submitted (failure prediction of an HVDC line without component-specific sensors).
Working on a program to convert time series measurement into CIM-XML files.
Work on a warning system using meter data, that sends an alarm to the DSO when household loads change significantly, and indicates the probable cause of the change.

Event log

2019-2020. Research visitor at the National Institute of Informatics (NII), 11 September 2019-17 January 2020, Tokyo, Japan.

2019. IEEE Big data conference, 9-12 December 2019, Los Angeles (USA).

2020. 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 18-21 August 2020, Liege, Belgium.

2021. Defense of Licentiate thesis entitled: "From data collection to electric grid performance : How can data analytics support asset management decisions for an efficient transition toward smart grids?", 19 April 2021

2021. IEEE PowerTech Conference, 28 June - 2 July 2021, Madrid, Spain

Project reference-group

Nilanga Abeywickrama,  ABB Transformers
Tor Laneryd,  ABB Transformers
Robert Saers,  ABB Transformers
Lars Enarsson,  Ellevio
Erik Lejerskog,  Ellevio
Malin Wihlén,  Ellevio
Susanne Stjernfeldt,  Energiforsk AB
Åsa Elmqvist,  Vindforsk/Energiforsk
Claes Ahlrot,  E.On
Ola Ivarsson,  E.On
Thomas Welte,  Sintef Energi
Matz Tapper,  Svensk Energi
Tommie Lindquist,  Svenska kraftnät
Milan Radosavljević,  Svenska kraftnät
Erik Jenelius,  Trafik och logistik KTH
Kitimbo Andrew,  Vattenfall
Anna Lilly Brodersson,  Vattenfall
Fredrik Carlsson,  Vattenfall
Ying He,  Vattenfall
Johan Öckerman,  Vattenfall

MSc etc theses connected to the project

Sindhu Kanya Nalini Ramakrishna, 2020, "Component importance indices and failure prevention using outage data in distribution systems" link.

Hugo Vincenti, "Using sensors to improve maintenance scheduling of power lines in the distribution grid: economic and ecologic analysis of predictive maintenance". Started in September 2021.

Publications by this researcher

See alternatively the researcher's full DiVA list of publications, with options for sorting.
Publications in journals and conferences usually will not show until a while after they are published.

Profitable sensor network design in the distribution grid
Sylvie Evelyne Koziel,   Patrik Hilber.
2023,   2023 IEEE Belgrade PowerTech, PowerTech 2023, Belgrade, Serbia, Jun 25 2023 - Jun 29 2023

From data collection to electric grid performance : How can data analytics support asset management decisions for an efficient transition toward smart grids?
Sylvie Evelyne Koziel.
2021,   Thesis (Licentiate), KTH Royal Institute of Technology, TRITA-EECS-AVL 2021:22

Investments in data quality : Evaluating impacts of faulty data on asset management in power systems
Sylvie Evelyne Koziel,   Patrik Hilber,   Per Westerlund,   Ebrahim Shayesteh.
2021,   Applied Energy, vol. 281

Component ranking and importance indices in the distribution system
Sindhu Kanya Nalini Ramakrishna,   Sylvie Evelyne Koziel,   David Karlsson,   Gustav Stenhag,   Patrik Hilber.
2021,   14th IEEE PowerTech Conference

A review of data-driven and probabilistic algorithms for detection purposes in local power systems
Sylvie Evelyne Koziel,   Patrik Hilber,   R. Ichise.
2020,   2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

Forecasting cross-border power exchanges through an HVDC line using dynamic modelling
Sylvie Evelyne Koziel,   Patrik Hilber,   Per Westerlund,   E. Shayesteh.
2019,   2019 IEEE International Conference on Big Data, Big Data 2019, 9 December 2019 through 12 December 2019

Application of big data analytics to support power networks and their transition towards smart grids
Sylvie Evelyne Koziel,   Patrik Hilber,   R. Ichise.
2019,   2019 IEEE International Conference on Big Data, Big Data 2019, 9 December 2019 through 12 December 2019

Publication list last updated from DiVA on 2024-01-10 15:21.

Page started: 2018-09-15
Last generated: 2024-01-10