Prof. Lina Bertling of KTH Royal Institute of Technology

LECTURE ABSTRACT

This lecture will delve into the intricacies of managing the life cycle costs associated with clean energy transition technologies. Prof. Bertling Tjernberg will discuss her innovative approach captured in the framework of Reliability-Centred Asset Management (RCAM), emphasizing the integration of circular economy principles and bridging the gap between theoretical models and practical applications. Prof. Bertling will share insights from her pioneering research in optimizing maintenance procedures, particularly in the context of wind turbines, discussing some models for condition-based and predictive maintenance, leveraging machine learning tools for enhanced efficiency and reliability. As a result, a systematic approach to maintenance management will be presented as a game-changer in modern power systems, which correlates component maintenance with overall system reliability.

Biography:

Personal Statement: 

The passion that drives my work is being able to contribute to the energy transition for a sustainable society. My scientific area of interest is applied mathematics with a focus on reliability theory and optimization with various power grid applications. The proposed models are captured in the framework of the reliability-centred asset management RCAM, which also includes the importance of a circular economy. In my work, I strive to bridge gaps between theory and practice, between academics, industry and decision-makers. I believe in an inclusive and multicultural working environment and society. It will be my pleasure to contribute to the Series Seminar on Enabling Technologies for Clean Energy Transition by giving a lecture on “Management and Life Cycle Cost of Clean Energy Transition Technologies”

Personal details:

Since 2013 I am a professor in Power Grid Technology at the Royal Institute of Technology KTH and since the end of 2017 I am the Director of the KTH Energy platform. My research focuses on using reliability theory, optimization and machine learning models for the prediction of maintenance efforts with various power system and equipment applications. I have recently been elected as a Fellow of the Royal Academy of Engineering IVA and I am a Senior member of IEEE. I received the 2021 Power Woman of the Year award for her work as a "strong force and guarantor of more equal skills provision in today's and future sustainable energy systems".  

Key outputs, contributions, and achievements:

I have contributed to developing modern power systems from the field of power system reliability, with a thorough and rigorous systematic approach to maintenance management, which relates component maintenance to system reliability. My thesis on distribution system maintenance was one of the first published works in this field and she proposed the RCAM framework and validated the model through detailed application studies for distribution systems. I have been a pioneer in the field of optimizing maintenance procedures of wind turbines making significant contributions to the development of new models for condition-based and predictive maintenance and to the application of machine learning tools for wind turbine components.

Email:

Address:TEKNIKRINGEN 33, , Sweden, Sweden, 9220