The health index has traditionally been devised and calculated for individual assets within a power system. This index provides vital details about an asset’s overall health and allows for a standardized comparison among various assets. However, the intricate nature of power systems poses significant challenges when trying to adapt this methodology for a broader, global power system health index. To tackle this obstacle, this paper proposes an innovative framework for evaluating power system health. The framework’s primary purpose is either to monitor the performance of a power system within a defined jurisdiction (such as a country, region, or utility) over time and identify trends/changes or to compare the performance across various jurisdictions. This paper further presents a comprehensive overview of key concepts that play a vital role in determining power system health. These include the driving factors, performance metrics, and associated costs, all of which are under the careful supervision of asset management. Special attention is given to the physical dimensions of the security of electricity supply, which represent the performance-based aspect of power system health and constitute the foundation for the power system health index. Each performance-based dimension is thoroughly reviewed, and a list of relevant key performance indicators is provided for every dimension. 

Data analysis plays a pivotal role in identifying patterns and relationships within data sets. By examining historical outage statistics in power systems, trends in system performance can be revealed, contributing to a better understanding of its behavior. Furthermore, by understanding the past performance of the power system, utility companies can make better decisions to enhance system reliability and resilience. This study investigates outage statistics in the Swedish power system from 2009 to 2019 and examines in depth the reporting mechanism. The data is clustered and analyzed according to three different criteria: voltage level of the breaking device, cause of the failure, and faulty equipment. Although the presented overview highlights key trends in system performance, the analysis has uncovered issues related to data quality and availability, such as missing values and inconsistencies that require further attention.

As a result of global warming, the frequency of bad weather events has increased raptly, but so has the demand for more reliable power supply. This study investigates the Swedish power distribution system’s resilience towards certain weather conditions such as wind, lightning, rain etc. The input data is all unplanned disturbances gathered from the Swedish energy companies (Energiföretagen Sverige) between 2015 and 2019. After sorting and analyzing the data, the results are then compared to the weather data from SMHI (Swedish Meteorological and Hydrological Institute). The results show that on average 21% of unplanned outages are related to weather conditions in Sweden. Of the weather phenomena studied, wind and lightning are significantly affecting the resilience of the power system. One way to prevent outages, especially in lower voltage distribution systems, where most disturbances occur, is to improve the maintenance of the system.

This study investigates outage statistics in the Swedish power system. More specifically, this paper delves into the critical analysis and enhancement of data quality, focusing on inconsistencies and missing values, i.e. unknown outage causes and unidentified faulty equipment. By carefully examining the data, noticeable gaps and deficiencies are revealed. Thus, a format for improving outage reporting using a database with 3 relations (outage summary, outage breakdown and customer breakdown) is proposed. In addition to a qualitative analysis of the data, various machine learning algorithms are explored and tested for their capability to predict the unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and facilitating deeper, more accurate analytical capabilities. The findings and proposals within this work not only illuminate the current challenges within outage data management but also pave the way for more robust, data-driven decision-making in outage management and policy formation.