Trial Lecture and Thesis Defense - Abd Alelah Derbas

Master of Science Abd Alelah Derbas will hold the Trial Lecture and Thesis Defense for the PhD degree in Science on Friday, November 20, 2026.

Trial Lecture

Takes place 10:15 - 11:15.

« Microgrids for Disaster Resilience: Planning, Emergency Operation, and Power Restoration »

Thesis Defense

Takes place 12:15 - 15:00.

« Novel Adaptive AI Control Strategies for Optimal Performance of Resilient Renewable Microgrids: AI Applications in Microgrids: Adaptive Control, Power Sharing, and Stability »

Abstract

The increasing integration of renewable energy sources, battery energy storage systems, electric vehicles, and converter-interfaced distributed energy resources is creating more decentralized and dynamically complex power systems. Microgrids provide an effective architecture for integrating these resources, especially in islanded and weak-grid operation, but conventional fixed-parameter control may suffer from voltage deviations, inaccurate current or power sharing, poorly damped oscillations, and degraded transient performance under changing operating conditions.

This dissertation develops and evaluates artificial-intelligence-based adaptive control strategies for converter-dominated renewable microgrids. The proposed methods retain established converter-control structures while using AI as a supervisory adaptation layer for physically meaningful control variables. The work includes ANN-based tuning of proportional-resonant voltage-controller gains and biomimetic valence learning for adaptive droop control in islanded AC microgrids

Across the investigated scenarios, the proposed approaches improve voltage regulation, current and power sharing, oscillation damping, and transient response relative to conventional baselines where such comparisons are available. The grid-forming studies demonstrate bounded and well-damped electrical behavior under the investigated weak-grid disturbances, without claiming universal superiority over fixed-parameter control. Overall, the thesis shows how AI can be integrated with conventional converter control to support adaptive, stability-aware, and predominantly local-measurement-based operation, with decentralized or communication-light implementation where applicable.

Supervisory Committee

• Associate Professor Chiara Bordin, Department of Computer Science, UiT The Arctic University of Norway (Main Supervisor)
• Professor Frede Blaabjerg, Department of Energy, Aalborg University
• Associate Professor Sambeet Mishra, Department of Electrical Engineering, IT and Cybernetics, University of South-Eastern Norway

Evaluation Committee

• 1st Opponent: Dr. Abderezak Lashab, Project Management Engineering, Hitachi Energy Denmark A/S, Denmark
• 2nd Opponent: Associate Professor Subhadra Tiwari, Department of Electric Energy, Norwegian University of Science and Technology (NTNU), Norway
• Internal member and leader of the committee: Professor Eirik Årsand, Department of Computer Science, UiT The Arctic University of Norway

Streaming

The Trial Lecture and Thesis Defense will be streamed via Panopto:

Trial Lecture (10:15 - 11:15)
Watch the Trial Lecture

Thesis Defense (12:15 - 15:00)
Watch the Thesis Defense

Thesis

View the thesis in Munin