Manuel Sathyajith Mathew has followed the PhD Programme at Faculty of Engineering and Science, Specialisation in Information and Communication Technology (ICT), scientific field Artificial Intelligence.
- Trial lecture starts at 10:15
- Public defence starts at 12:15
Title of trial lecture: "Deep Penetration of Renewable Energy Systems in Power Grids: Challenges and Main Technologies"
Disputation chair: Dean Anne Cathrine Gjærde
Assessment committee:
- First opponent: Professor Eleonora Riva Sanseverino, University of Palermo, Italy
- Second opponent: Professor Arvind Keprate, OsloMet, Norge
- Chair of assessment committee: Associate Professor Rebekka Olsson Omslandseter, UiA
Supervisors in the doctoral work:
- Main supervisor: Professor Christian Omlin, UiA
- Co-supervisor: Surya Teja Kandukuri, UiA
Summary of thesis
Wind energy plays a crucial role in the global clean energy transition. While substantialwind energy capacity has been added to the electric grids in the recentyears, a significant number of wind farms in operation in Europe and around theworld are approaching their end of designed lifetime. At the end of life, wind farmoperators are faced with three options: (1) lifetime extension, (2) repowering, or (3)decommissioning. Effective wind farm asset management strategies can help informthese decisions, ensuring minimal loss of valuable resources like viable assets, andresourceful sites.This work develops an aging-aware multi scale analytics framework for effective managementof wind farm assets. Contrary to the traditional siloed approach of technicalasset management, the work puts forth a framework integrating both technical andsocial dimensions of asset management. The technical dimension is tackled at multiplescales: (1) an automatic fault detection and localization system developed usingdeep learning-based model using minimal new sensor deployment, (2) a novel deeplearning-based approach to estimating the age-related degradation in performanceof a wind turbine, and (3) a methodology to estimate the capacity factor of windfarms using SCADA data and deep learning-based model which could further beexpanded to analyze the degradation of the systems at a farm level.However, understanding that even a technically sophisticated asset managementstrategy by itself can be insufficient during the lifecycle of a wind power project,this work further utilizes the tools of systems thinking framework to analyze the phenomenon of formation of public perception of these capital intensive assets ina Norwegian context. Together, these four pillars of this work form a toolkit foreffective asset management strategy for a wind power project. that addresses both technical and social dimensions.
What to do as an online audience member
The disputation is open to the public. To follow the trial lecture and the public defence online, register on Zoom.
We ask online audience members to join no earlier than 10 minutes in advance. After these times, you can leave and rejoin the meeting at any time.
Opponent ex auditorio:
Deadline for the public to pose questions is during the break between the two opponents. Questions ex auditorio can be submitted to Kristine Evensen Reinfjord.
Contact person
- kristine.reinfjord@uia.no
- Phone
- +47 37 23 30 14