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Data management improvements in the electrical grid: a pathway to a smarter cyber-physical system
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.ORCID iD: 0000-0001-7537-5577
2024 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Datahanteringsförbättringar i elnätet : en väg till ett smartare cyberfysiskt system (Swedish)
Abstract [en]

The current power system is a network of electrical components forming a physical system. It is experiencing changes, such as the deployment of electric vehicles and distributed energy sources. Meanwhile, cybernetworks are becoming coupled into the physical grid to an increasing degree. This transformation of electrical grids into smart grids is often the focus of research literature. It is strongly linked to the concurrent evolution of the cyberinfrastructure, which is the focus of this thesis. It aims to support the incremental upgrade of data systems, taking real-world constraints into account, as well as opportunities offered by sensors and machine learning. With this background, two research questions have been identified: i) How to use available data more efficiently to improve asset management? and ii) How much and which kind of new data are actually needed?

With regard to the first research question, ways to use available data more efficiently are investigated in collaboration with a distribution system operator (DSO). One option is for DSOs to adopt best practices in terms of data management, which include: de-silo data, enhance reporting practices, and automatize tasks. To illustrate how combining available data may deliver additional relevant information, criticality indices have been calculated and assigned to components of a substation, by combining outage data, operation data and network diagram. Another option is to develop machine learning algorithms to perform specific or new tasks. A failure warning system has been developed using machine learning to leverage existing data about power components. It provides component-specific red flags, in situations where the widespread installation of component-specific sensors is unrealistic.

With regard to the second research question, a methodology has been developed to identify which data are actually needed. The approach is scenario-based, and formalizes mathematically the relations between data, grid management and grid performance. It has been applied in three studies. One study uses the methodology to explore impacts of data quality on grid management costs, and to evaluate the most profitable amount of investment needed into data quality improvement. Another study provides a tool to evaluate the profitability of investments in condition-monitoring sensors. A third study investigates how data granularity affects decisions in grid upgrades, and ultimately the quality of power supply, and proposes a way to decide where, how, and how much to upgrade the cyberinfrastructure.

In summary, this thesis: i) shows the importance of data for grid performance; ii) conceptualizes, formalizes mathematically, and quantifies relations between data, grid management, and grid performance; iii) develops new approaches to support the transformation of the cyberinfrastructure needed for a transition to smart grids.

Abstract [sv]

Det nuvarande kraftsystemet är ett nät av elektriska apparater som utgör ett fysiskt system. Det upplever förändringar, till exempel ökande antal elfordon och distribuerade energikällor. Samtidigt kopplas cybernätverk in i det fysiska nätet i allt högre grad. Dessa förändringar förvandlar elnätet till ett smart nät som är ofta i fokus för forskningslitteraturen. Den omvandlingen är starkt kopplat till den samtidiga utvecklingen av cyberinfrastrukturen, som är fokus för denna avhandling. Det syftar till att stödja den stegvisa uppgraderingen av datasystem, med hänsyn till verkliga begränsningar, såväl som möjligheter som sensorer och maskininlärning erbjuder. Med denna bakgrund har två forskningsfrågor identifierats: i) Hur kan man använda tillgängliga data mer effektivt för att förbättra nätförvaltning? och ii) Hur mycket och vilken typ av nya data behövs egentligen?

När det gäller den första forskningsfrågan undersöks sätt att använda till-gängliga data mer effektivt i samarbete med en elnätsägare. Ett alternativ är att elnätsägare använder erkänt välfungerande metoder när det gäller datahantering, som inkluderar: kombinera data från olika källor, förbättra rapporteringsmetoder och automatisera uppgifter. För att illustrera hur en kombination av tillgängliga data kan ge ytterligare relevant information, har kritikalitetsindex beräknats och tilldelats komponenter i en transformatorstation genom att kombinera avbrottsdata, driftdata och nätstruktur. Ett annat alternativ är att utveckla maskininlärningsalgoritmer för att genomföra specifika eller nya uppgifter. Ett felvarningssystem har utvecklats med hjälp av maskininlärning för att utnyttja befintliga data om elkraftapparater. Det ger apparatspecifika röda flaggor, i situationer  där en utbredda installation av särskilda sensorer är orealistisk.

När det gäller den andra forskningsfrågan har en metodik utvecklats för att identifiera vilka data som faktiskt behövs. Tillvägagångssättet är scenariobaserat och formaliserar matematiska relationer mellan data, näthantering och nätprestanda. Det har tillämpats i tre studier. En studie använder metoden för att undersöka inverkan av datakvalitet på nätförvaltningskostnader och för att identifiera det mest lönsamma investeringsbeloppet för datakvaliteten förbättring. En annan studie föreslår ett verktyg för att utvärdera lönsamheten för investeringar i tillståndsövervakningssensorer. En tredje studie utreder hur datagranularitet påverkar beslut om nätuppgraderingar  , och i slutändan kvaliteten på strömförsörjningen, och föreslår ett sätt att bestämma var, hur och hur mycket cyberinfrastrukturen ska uppgraderas.

Denna avhandling: i) visar betydelsen av data för nätprestanda ; ii) konceptualiserar, formaliserar matematiskt och kvantifierar relationer mellan data, näthantering samt nätprestanda; iii) utvecklar nya metoder för att stödja omvandlingen av den cyberinfrastruktur som behövs för en övergång till smarta nät.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2024. , p. 67
Series
TRITA-EECS-AVL ; 2024:27
Keywords [en]
asset management, data management, data analytics, distribution, grid operation and planning, machine learning, cyberinfrastructure upgrade
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-344438ISBN: 978-91-8040-866-0 (print)OAI: oai:DiVA.org:kth-344438DiVA, id: diva2:1845120
Public defence
2024-04-08, F3 (Flodis), Lindstedtsvägen 26, Stockholm, 10:00
Opponent
Supervisors
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage
Note

QC 20240321

Available from: 2024-03-21 Created: 2024-03-17 Last updated: 2024-03-25Bibliographically approved
List of papers
1. A review of data-driven and probabilistic algorithms for detection purposes in local power systems
Open this publication in new window or tab >>A review of data-driven and probabilistic algorithms for detection purposes in local power systems
2020 (English)In: 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
Feature extraction, Power systems, Data mining, Task analysis, Classification algorithms, Prediction algorithms, Principal component analysis, anomaly detection, fault location, load disaggregation, machine learning, review
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-292206 (URN)10.1109/PMAPS47429.2020.9183634 (DOI)000841883300072 ()2-s2.0-85091343794 (Scopus ID)
Conference
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 18-21 August 2020, Liege, Belgium
Note

QC 20230921

Available from: 2021-03-30 Created: 2021-03-30 Last updated: 2024-03-17Bibliographically approved
2. Component ranking and importance indices in the distribution system
Open this publication in new window or tab >>Component ranking and importance indices in the distribution system
Show others...
2021 (English)In: 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2021, article id 9494968Conference paper, Published paper (Refereed)
Abstract [en]

Monitoring the condition of components helps taking preventive actions to avoid failures, and increases reliability. However, performing such monitoring for all components of the distribution grid is prohibitively expensive. Instead, distribution system operators could focus efforts only on the most critical components. In particular, importance indices enable to prioritise components according to a chosen criterion, and to adapt monitoring strategies. This study presents methods to rank grid components using outage data. The importance indices are based on: 1) de-energisation time; 2) frequency of failures; 3) disconnected power; 4) energy not supplied and 5) customer outage time. The results depend largely on the time period of the outage data considered for analysis. Some components' rank varies with the chosen criterion. This indicates that they are critical with respect to a specific criterion. Other components are ranked high with all the methods, which means that they are critical, and need focused monitoring.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Keywords
Customer outage time, de-energisation time, disconnected power, distribution system, Energy Not Supplied, failure, outages
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-292207 (URN)10.1109/PowerTech46648.2021.9494968 (DOI)000848778000217 ()2-s2.0-85112365051 (Scopus ID)
Conference
2021 IEEE Madrid PowerTech, PowerTech 2021, 28 June 2021 through 2 July 2021
Note

QC 20220927

Part of proceedings: ISBN 978-1-6654-3597-0

Available from: 2021-03-26 Created: 2021-03-26 Last updated: 2024-03-17Bibliographically approved
3. Failure warning system for individual electric power components without component specific sensor data
Open this publication in new window or tab >>Failure warning system for individual electric power components without component specific sensor data
(English)In: Article in journal (Refereed) Submitted
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-344435 (URN)
Note

QC 20240405

Available from: 2024-03-17 Created: 2024-03-17 Last updated: 2024-04-05Bibliographically approved
4. Investments in data quality: Evaluating impacts of faulty data on asset management in power systems
Open this publication in new window or tab >>Investments in data quality: Evaluating impacts of faulty data on asset management in power systems
2021 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 281, article id 116057Article in journal (Refereed) Published
Abstract [en]

Data play an essential role in asset management decisions. The amount of data is increasing through accumu-lating historical data records, new measuring devices, and communication technology, notably with the evolution toward smart grids. Consequently, the management of data quantity and quality is becoming even more relevant for asset managers to meet efficiency and reliability requirements for power grids. In this work, we propose an innovative data quality management framework enabling asset managers (i) to quantify the impact of poor data quality, and (ii) to determine the conditions under which an investment in data quality improvement is required. To this end, an algorithm is used to determine the optimal year for component replacement based on three scenarios, a Reference scenario, an Imperfect information scenario, and an Investment in higher data quality scenario. Our results indicate that (i) the impact on the optimal year of replacement is the highest for middleaged components; (ii) the profitability of investments in data quality improvement depends on various factors, including data quality, and the cost of investment in data quality improvement. Finally, we discuss the implementation of the proposed models to control data quality in practice, while taking into account real-world technological and economic limitations.

Place, publisher, year, edition, pages
Elsevier BV, 2021
Keywords
Asset management, Component replacement, Data quality costs, Electric power distribution, Optimization, Trade-off
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:kth:diva-288730 (URN)10.1016/j.apenergy.2020.116057 (DOI)000591382100011 ()2-s2.0-85095704083 (Scopus ID)
Note

QC 20210113

Available from: 2021-01-13 Created: 2021-01-13 Last updated: 2024-03-17Bibliographically approved
5. Profitability of Condition Monitoring in the Electric Distribution Grid
Open this publication in new window or tab >>Profitability of Condition Monitoring in the Electric Distribution Grid
2023 (English)In: IET Conference Proceedings, Institution of Engineering and Technology , 2023, p. 362-366Conference paper, Published paper (Other academic)
Abstract [en]

The deployment of sensors enables the development of condition-based maintenance, as opposed to the traditional time-based and corrective maintenance. This work explores the conditions under which the use of sensors to improve maintenance scheduling on overhead lines is economically profitable. We propose a novel methodology that converts sensor measurements into an asset condition assessment, and then into a maintenance decision. The cost of predictive maintenance is then compared to the cost of corrective maintenance over several decades, ultimately allowing to evaluate the profitability of investing into sensors. This work enables to identify the parameter values that result in profitable investments in sensors. The results show that the use of sensors is particularly justified for short-lived assets, supplying many clients.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2023
National Category
Energy Systems
Identifiers
urn:nbn:se:kth:diva-342405 (URN)10.1049/icp.2023.0309 (DOI)2-s2.0-85181536809 (Scopus ID)
Conference
27th International Conference on Electricity Distribution, CIRED 2023, Rome, Italy, Jun 12 2023 - Jun 15 2023
Note

QC 20240118

Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-03-17Bibliographically approved
6. Profitable sensor network design in the distribution grid - updated
Open this publication in new window or tab >>Profitable sensor network design in the distribution grid - updated
2024 (English)Other (Other academic)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-344437 (URN)
Available from: 2024-03-17 Created: 2024-03-17 Last updated: 2024-03-22Bibliographically approved
7. Data Impact on Cyber-physical Planning and Operation of Active DistributionNetworks
Open this publication in new window or tab >>Data Impact on Cyber-physical Planning and Operation of Active DistributionNetworks
Show others...
(English)In: Article in journal (Refereed) Submitted
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-344436 (URN)
Note

QC 20240405

Available from: 2024-03-17 Created: 2024-03-17 Last updated: 2024-04-05Bibliographically approved

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Koziel, Sylvie Evelyne

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