In power system asset management, component data is crucial for decision making. This thesis mainly focuses on two aspects of asset data: data quality and data availability.

The quality level of data has a great impact on the optimality of asset management decisions. The goal is to quantify the impact of data errors from a maintenance optimization perspective using random population studies. In quantitative terms, the impact of data quality can be evaluated financially and technically. The financial impact is the total maintenance cost per year of a specific scenario in a population of components, whereas the technical impact is the loss of a component's useful technical lifetime due to sub-optimal replacement time. Using Monte-Carlo simulation techniques, those impacts are analyzed in a case study of a simplified random population of independent and non-repairable components. The results show that missing data has a larger impact on cost and replacement year estimation than that of under- or over-estimated data. Additionally, depending on problem parameters, after a certain threshold of missing data probability, the estimation of cost and replacement year becomes unreliable. Thus, effective decision making for a certain population of components requires ensuring a minimum level of data quality.

Data availability is another challenge that faces power system asset managers. Data can be lacking due to several factors including censoring, restricted access, or absence of data acquisition. These factors are addressed in this thesis from a decision making point of view through case studies at the operation and maintenance levels. Data censoring is handled as a data quality problem using a Monte-Carlo simulation. While the problems of restricted access and absence of data acquisition are studied using event trees and multiphysics modelling. 

While the quantitative data quality problem can be abstract, and thus applicable to different types of physical assets, the data availability problem requires a case-by-case analysis to reach an effective decision making strategy.

The current shift towards a higher degree of data-driven decision making in power system asset management highlights the importance of asset data. This thesis identifies, investigates, and proposes methods for data-related research gaps that are encountered by asset managers. These research gaps are in data availability and data quality. 

It is challenging to generalize the data availability problem on an abstract level. Thus, data availability is studied through three different case studies. Each case study addresses a factor that contributes to data availability problems. Data censoring is modeled as a data quality problem using a Monte-Carlo simulation. Lack of access to and acquisition of data are studied through event tree analysis and multiphysics modelling. These case studies reveal that even in a low data availability environment, informed decision making is feasible. 

Monte-Carlo simulation techniques are powerful when analyzing the data quality problem. Asset data quality is studied based on two perspectives; namely, maintenance optimization and reliability evaluation. First, using random population studies shows that data quality can have a notable financial and technical impact on maintenance optimization. A critical finding is that missing data can lead to distortions in estimates of the optimal replacement time of a component. It is shown that there exists a certain threshold of missing data proportion beyond which maintenance optimization becomes unreliable. The specific percentage value of this threshold depends on the failure model parameters. Second, incorporating the data quality model in a reliability test system simulation shows that the impact on the annual estimation of system- and energy-oriented reliability indices is nearly non-existent.

Finally, this thesis introduces a method to rank component types based on data quality importance. The data quality importance (DQI) ranking is derived from the Weibull function’s sensitivity to data errors. This method indicates that distortions in Weibull parameters have a non-linear impact on maintenance optimization. This leads to a conclusion that investments in data quality must be allocated based on the DQI ranking of a certain component. Reaching the right level of data quality for a component leads to efficient decision making. 

Den nuvarande övergången mot en högre grad av datadrivet beslutsfattande inom förvaltning av kraftsystem belyser vikten av tillgång till data. Denna avhandling identifierar och undersöker och föreslår metoder för datarelaterade forskningsluckor som kapitalförvaltare möter. Dessa forskningsluckor finns i datatillgänglighet och datakvalitet.

Det är utmanande att generalisera datatillgänglighetsproblemet på en abstrakt nivå. Datatillgänglighet studeras alltså genom tre olika fallstudier. Varje fallstudie tar upp en faktor som bidrar till datatillgänglighetsproblem.  Datacensurering modelleras som ett datakvalitetsproblem med hjälp av en Monte-Carlo-simulering. Bristande tillgång till och inhämtning av data studeras genom händelseträdsanalys och multifysisk modellering. Dessa fallstudier visar att även i en miljö med låg datatillgänglighet är välgrundat beslutsfattande möjligt.

Monte-Carlo simuleringstekniker är kraftfulla när man analyserar datakvalitetsproblemet.  Tillgångsdatakvalitet studeras utifrån två perspektiv; näm-ligen underhållsoptimering och tillförlitlighetsutvärdering.  För det första visar användning av slumpmässiga befolkningsstudier att datakvalitet kan ha en betydande ekonomisk och teknisk inverkan på underhållsoptimering.  En kritisk upptäckt är att saknad data kan leda till förvrängningar i uppskattningar av den optimala utbytestiden för en komponent. Det har visat sig att det finns en viss tröskel för andelen saknad data, bortom vilken underhållsoptimering blir opålitlig.  Det specifika procentvärdet för denna tröskel beror på felmodellens parametrar.  För det andra, att införliva datakvalitetsmodellen i en simulering av tillförlitlighetstestsystem visar att effekten på den årliga uppskattningen av system- och energiorienterade tillförlitlighetsindex är nästan obefintlig.

Slutligen introducerar denna avhandling en metod för att rangordna komponenttyper baserat på datakvalitetens betydelse.  Rankningen av datakvalitetens betydelse (DQI) härleds från Weibull-funktionens känslighet för datafel.  Denna metod indikerar att förvrängningar i Weibull-parametrar har en icke-linjär inverkan på underhållsoptimering. Detta leder till slutsatsen att investeringar i datakvalitet måste allokeras utifrån DQI-rankningen av en viss komponent.  Att nå rätt nivå av datakvalitet för en komponent leder till effektivt beslutsfattande.