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.

In power system asset management, component datais crucial for decision making. Consequently, the quality level ofdata has a great impact on the optimality of asset managementdecisions. The goal of the paper is to quantify the impact ofdata errors from a maintenance optimization perspective usingrandom population studies. In quantitative terms, the impact ofdata quality can be evaluated financially and technically. In thispaper, the financial impact is the total maintenance cost per yearof a specific scenario in a population of components, whereasthe technical impact is the loss of a component’s useful technicallifetime due to sub-optimal replacement time. Using Monte-Carlosimulation techniques, those impacts are analyzed in a case studyof a simplified random population of independent and nonrepairablecomponents. The results show that missing data hasa larger impact on cost and replacement year estimation thanthat of under- or over-estimated data. Additionally, dependingon problem parameters, after a certain threshold of missingdata probability, the estimation of cost and replacement yearbecomes unreliable. Thus, effective decision making for a certainpopulation of components requires ensuring a minimum level ofdata quality.

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.

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.

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. 

 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. 

 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. 

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.