We are looking for a motivated PhD candidate to work on detection of cyber-malicious actions in distributed power grid systems at INSA Lyon (France). This thesis is co-supervized by LAAS-CNRS.
Future energy networks will increasingly rely on interconnected and distributed infrastructures integrating photovoltaic systems, battery storage, buildings, electric vehicles, and local controllers. While this evolution is essential for the energy transition, it also creates new vulnerabilities to cyber-malicious actions targeting control signals, measurements, and local decision-making units.
The PhD will focus on the detection and classification of intelligent cyberattacks targeting local controllers in distributed power grid systems.
The main research directions include:
- Modelling distributed power grid systems and their local controllers
- Reviewing fault detection methods, including model-based, observer-based, and machine-learning approaches
- Identifying markers of intelligent, intentional, and malicious manipulations of detection systems
- Developing detection and classification methods to distinguish faults, disturbances, and cyberattacks
- Testing and validating the proposed approaches through simulation and experimental scenarios, with possible validation on the Grid4Mobility platform at INSA Lyon.
The position is funded for 36 months through a French doctoral contract. The PhD student will benefit from the French healthcare system, and affordable accommodation on or near the INSA Lyon campus may be possible depending on availability.
- Main location: INSA Lyon, Ampère Laboratory, Lyon, France
- Duration: 36 months
- Keywords: cyber-resilience, distributed power grids, attack detection, fault diagnosis, local controllers
Candidates with a background in control systems, power systems, signal processing, or machine learning of cyber-physical systems are encouraged to apply.
Interested candidates can contact us at cedric.escudero@insa-lyon.fr and paolo.massioni@insa-lyon.fr with a CV, academic transcripts, and a short motivation statement.
