Optimal asset management data - Components swegrids-logo

SweGRIDS research area Controllable Power Components
SweGRIDS project code CPC6
Project type PhD
Status running
Researcher Sylvie Koziel   (webpage)
University KTH (EME)
Project period 2018-09-15 to 2023-03-15   
Project supervisor Patrik Hilber   (webpage)
Industrial sponsors E.ON


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Project abstract

The electricity system is changing at a fast pace and is getting more complex because of changing electricity generation structure and consumer behavior. At the same time, I&C technologies are also evolving at a fast pace, and are expected to play a big role in the energy transition, especially in the integration of fluctuating renewable power. One major challenge for power systems is therefore to design an efficient data architectural framework with the following properties: The data acquisition infrastructure must support the power network by providing geographically and temporally diverse data. This deluge of data must be transformed into useful operational information and business intelligence. The voluminous big data must not only be collected, analyzed and stored in an efficient way, but also protected against cyber attacks.

My PhD project aims at building a framework for a monitoring system that can be used as a decision-making tool for optimal asset management strategies in power systems. It focuses on data analytics, and should give power companies the right level of information to apply dynamic adjustments to the system, thus allowing better control and optimizing the use of their assets.

It is necessary to distinguish between two time scales: medium-term asset management, which is defined as the reliability-centered maintenance management (components’ preventive maintenance or replacement). For this time scale, the most suitable methods to assess the performance and status of individual components, and the relevant data that need to be collected and processed will be identified. Long-term asset management is defined as the strategic planning, which encompasses network configuration, and adaptation to future evolution of the power system. For this time scale, indicators to monitor the evolution of the system will be identified, and analytics to assess needed future changes to the power system will be developed.

The approach includes several steps: first, the identification of asset management strategies, of challenges that distribution companies might experience in the future, and the gaps between the current situation and future possible developments. Second, the design of a data system that will support the decision making for asset management under uncertainty. Third, the basic structure of the data management system will be refined and optimized. In particular, this will enable stakeholders to find the “right” level of data detail, i.e. the level compatible with their willingness to pay and take risks.


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.


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.


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.


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.

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

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

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 2021-09-18 22:00.


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Page started: 2018-09-15
Last generated: 2021-09-18