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.

Power system asset management involves several multidisciplinary activities to ensure the reliable, efficient, and safe operation of power equipment. One aspect of effective asset management is that planning and decision making have to be data driven. However, the use of data comes with a set of challenges. In this paper, we review the state-of-the-art of asset management, data management, and their links to power systems in particular. Previous literature is reviewed methodically based on keyword search and setting scores for different parameters of interest. Asset management strategies and activities are reviewed along with trends in data management. The review concludes with an observation that data quality and data availability are the most pressing current challenges in power system asset management.

Certain rare events can have a drastic impact on power systems. Such events are generally known as high-impact low-probability (HILP) events. It is challenging to predict the occurrence of a HILP event mainly due to lack of data or sparsity and scarcity of data points. Yet, it is essential to implement an evidence-driven asset management strategy. In this paper, event tree analysis is used to assess the risk of power transformer failure due to a geomagnetically induced currents (GIC). Those currents are caused by geomagnetic disturbances in Earth’s magnetic field due to solar activity. To assess the impact on power transformers, an understanding of the mechanism and sequence of sub-events that lead to failure is required to be able to construct an event tree. Based on the constructed event tree, mitigation actions can be derived. GIC blockers or reducers can be used. However, that would require extensive installation and maintenance efforts, and the impact on system reliability has to be studied. Also, such technology is still in its infancy and needs extensive validation. A suggested alternative is to combine early warning data from solar observatories with a load management plan to keep transformers below their rated operation point such that a DC offset due to GIC would not cause magnetic core saturation and overheating. Load management and the risk of early warning false positives can incur a negative effect on reliability. Nevertheless, the risk assessment performed in this paper show that incorporating load management in asset planning is a viable measure that would offset the probability of catastrophic failure.

Advances in wireless power transfer technology provide the possibility to construct electrified roads and charge electric vehicles driving on the road. Dynamic charging mode enables the contactless interaction of electric vehicles with the power grid and has a promising prospect, but it may also bring about potential challenges to the power grid such as reliability deterioration. Electric buses serve as the forerunner to use this new charging mode due to their fixed driving patterns. Thus, it is needed to investigate its potential impact on power distribution system reliability. In this paper, first, the electric bus dynamic charging model is constructed, and then the impacts of this model on power distribution system reliability are studied. Simulation results indicate that compared with the non-dynamic charging mode, the electric bus dynamic charging mode does not cause additional deterioration to the reliability performance and has a slightly better effect. (C) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Dynamic line rating can be described as a method of overloading the power line within reliability and safety limits. Power line's loading limits can be increased, if its temperature is controlled to be below the maximum allowable conductor temperature, which is defined by the grid regulations. Dynamic rating brings additional uncertainties and risks to the grid operation due to high variability of weather conditions, which plays an essential role in determining real-time capacity limits. Power lines often are under the influence of risk factors related to power system performance, however, they could also be subjected to additional risks related to their mechanical structure. Overhead lines, which are composed of more than one stranded material, are exposed to increasing mechanical stress due to differences in thermal expansion characteristics of different materials. The reliability analysis of transient expansion/shrinkage of the material has identified the risks to the conductor mechanical strength that are associated with dynamic heating and cooling. This study determines an optimal dynamic line rating application, which not only would take into account electrical properties of the system and economic benefits, but would also minimize the aging of steel reinforced aluminum overhead lines. Alternatively to hourly line rating adjustment, 2 h, 3 h and 4 h ratings are suggested as possible way to decrease impact of DLR on conductor mechanical durability. Comparing the mechanical durability and cost benefits between different frequencies of loading limit adjustments, allows suggesting improvements to dynamic line rating application. 

Advances in wireless power transfer technology provide the possibility to construct electrified roads and charge electric vehicles driving on the road. Dynamic charging mode enables the contactless interaction of electric vehicles with the power grid and has a promising prospect, but it may also bring about potential challenges to the power grid such as reliability deterioration. Electric buses serve as the forerunner to use this new charging mode due to their fixed driving patterns. Thus, it is needed to investigate its potential impact on power distribution system reliability. In this paper, first, the electric bus dynamic charging model is constructed, and then the impacts of this model on power distribution system reliability are studied. Simulation results indicate that compared with the non-dynamic charging mode, the electric bus dynamic charging mode does not cause additional deterioration to the reliability performance and has a slightly better effect.

Dynamic Line Rating (DLR) is an emerging technology, which provides better utilization of power lines, by using real-time information on the weather parameters to dynamically adjust line rating limits. The power line capacity is highly dependent on its heat balance. The heat balance is influenced by external factors such as wind speed, ambient temperature, humidity, solar radiation and load. DLR analyses have shown high economical and reliability benefits from power system perspective. However, the mechanical stress on the conductor due to differences in thermal expansion characteristics of Aluminum and Steel materials could lead to faster ageing and mechanical damages. The study aims to provide better understanding of the risks associated with DLR application, which can affect conductor's mechanical lifetime. The reliability analysis of transient expansion and shrinkage of the material has identified the risks to the conductor mechanical strength that are associated with dynamic heating and cooling.

In the electric system, the consumption varies throughout the year, during the week and during the day. The consumption should be balanced by the production, which is not easy with solar power and wind power, as they lack storage like the dams for hydropower. Then these sources should be modelled as time series. The time series analyzed is the solar and wind power production in Sweden during 5 months. The method is called Peaks over Threshold or POT and it calculates the frequency of peaks above a certain threshold. It also determines the distribution of the size of the peaks, which is the generalized Pareto distribution in this case. Two different clustering methods are tried; one by Lindgren and the other one a modification of Leadbetter's method. The latter one gives the best fit. However, some ten years of data should be analyzed in order to include the seasonal effects.

 Data-driven decision making is essential for efficient power system asset management. Faulty data causes distortions in component failure model estimations. This leads to inaccurate maintenance cost minimization and sub-optimal component replacements. In this paper, we review existing standards and frameworks related to data quality and asset management. We then study the sensitivity of a maintenance optimization model to the Weibull distribution shaping parameter and observe a non-linear relation. Based on this study, we propose using the Weibull shaping parameter as a data quality importance index to rank power system components in terms of data requirements. 

 Data-driven planning and decision making are significant for effective asset management. In power systems, several data-related challenges are present. This paper investigates the effect of two main data quality issues, missing and inaccurate data, on reliability evaluation of a small power distribution system. We use the IEEE RBTS-Bus2 system as a model of a small distribution grid and to assess the asset age data quality impact on its reliability indices. Results show that, on average, faulty component age data does not have an impact on annual reliability evaluation.