Security of Electricity Supply in Power Systems
Establishing a Global Framework for Assessing Power System Health and Analyzing Outage Statistics in Sweden
Time: Mon 2024-02-19 13.00
Location: Kollegiesalen, Brinellvägen 8, Stockholm
Video link: https://kth-se.zoom.us/j/68367508107
Subject area: Electrical Engineering
Doctoral student: Sanja Duvnjak Zarkovic , Elektromagnetism och fusionsfysik
Opponent: Assistant Professor Christina Papadimitriou, Eindhoven University of Technology TUE
Supervisor: Patrik Hilber, Elektromagnetism och fusionsfysik; Ebrahim Shayesteh, Elektromagnetism och fusionsfysik
The primary objective of this thesis is to enhance the security of electricity supply by providing a holistic perspective and introducing a comprehensive framework for assessing power system health. This novel approach aims for a thorough evaluation of the system’s overall performance and well-being, using the physical dimensions of the security of supply as the foundation for a power system health index.
After establishing the theoretical framework, relevant and available data is collected in order to analyze and understand the system’s performance. By analyzing outage statistics in Sweden, the research identifies specific trends and performance metrics that can be further investigated and segmented according to various criteria. The insights gained from this research can, in turn, be used to inform proactive maintenance strategies and capacity planning, ultimately mitigating the risks of outages and ensuring a more reliable electricity supply.
Outage statistics are furthermore analyzed from the aspect of data quality, focusing on inconsistencies and missing values in the outage reports, 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 highlight the current challenges within outage data management and also lay the groundwork for a more comprehensive, data-driven approach in outage management and policy development.