Robust Real-Time Control & Estimation
Motivation
- Smart Energy Systems (SES) face critical challenges in reliability and efficiency due to increased uncertainties in multi-energy vector environments.
- Traditional real-time monitoring and control algorithms often become inaccurate or fail under the complex uncertainties present in SES.
- There is a pressing need for robust, adaptive, and data-driven control and estimation methods to enhance the resilience of energy systems against these uncertainties.
Objectives
- Analyze the impact of uncertainty on real-time monitoring and control in both multi- and single-energy vector Smart Energy Systems.
- Develop robust theoretical methods and practical solutions to enhance the resilience of real-time monitoring and control systems against uncertainties in SES.
- Prototype and test the proposed algorithms using real-life scenarios and industry-provided data to ensure their effectiveness in practical applications.
Individuals
Personal background
I have been accepted into a PhD program as a Marie Skłodowska-Curie fellow at TU Ilmenau, working on robust state estimation methods for Smart Energy Systems (SES). My academic background is in systems and control engineering, with expertise in robust control, data-driven control, and applications of control. I have experience in developing mathematical models and designing control algorithms for complex systems. My research focuses on enhancing the reliability and efficiency of SES by tackling uncertainties and disturbances through advanced estimation and control techniques. I am particularly interested in integrating data-driven and model-based approaches to optimize real-time monitoring and control strategies.
Research project
- Title: Robust procedures for state estimation of uncertain systems with disturbances attenuation
- Objectives: Development of robust state estimators for SES with disturbances and structured model uncertainties; Transfer of design strategies from robust control theory, such as H∞ control, that are dual to robust estimation; Case studies for typical multi-system scenarios.
- Expected outcomes: Efficient realization of the estimation algorithms on a real-word SES; Guidelines for selection and tuning of estimator parameters; Discretized reduced-order versions of the algorithms; Insight into properties/necessities to networked characteristics in SES.
Main affiliation
Personal motivation
I am motivated by the challenge of improving real-time monitoring and control in SES. The increasing damand and reliance on renewable energy sources and decentralized networks lead us to take advantage of robust estimation methods that can handle uncertainties and disturbances efficiently. My passion for control theory and optimization drives me to develop innovative solutions that enhance system robustness and stability. Through this research, I aim to bridge the gap between theoretical advancements in robust state estimation and practical applications in SES, contributing to more dependable and sustainable energy networks.
Personal background
Mohammad Hashemnezhad is a PhD student in the Dependable Smart Energy Systems (DENSE) program at Cyprus University of Technology (CUT). He obtained his master’s degree in Electrical Engineering from Tarbiat Modares University (TMU), where he focused on power systems and smart grid technologies. During his studies, he gained experience in power grid optimization and control, particularly in the integration of renewable energy sources. His research interests include data-driven control methodologies, decentralized optimization, and improving the resilience of active distribution networks.
Research project
- Title: Data-driven decentralised control design in Active Distribution Grids
- Objectives: Development of new methods for designing robust decentralized control of electricity distribution networks using data-driven methods; Increase fault tolerance of Smart Grids; Comparison with other state-of-the-art online learning control methods.
- Expected outcomes: Algorithms and software for custom design of data-driven controls in active distribution networks; set of requirements for security constrained training of local controls in electricity grids; experimental protocols for validation of data-driven control design in HIL environment.
Main affiliation
Supervisor team
Personal motivation
The transition toward decentralized and intelligent power systems requires innovative control strategies to ensure stability and efficiency. My motivation for this research stems from the need to develop robust, data-driven control solutions that enhance the operation of Active Distribution Networks. Through this project, I aim to contribute to the advancement of scalable and adaptive control techniques that bridge the gap between centralized and decentralized control schemes. The opportunity to work within an international, interdisciplinary research network like DENSE further motivates me to develop practical solutions that can be validated and implemented in real-world power systems.
Personal background
Moein Sarbandi is a PhD student in Automatic Control at École Centrale Nantes. He is conducting his research under the supervision of Prof. F. Plestan, Dr. M.A. Hamida, Dr. Machado , and Dr. Spanias as a member of the DENSE Doctoral Network and the LS2N Laboratory. He obtained his Master’s degree in Control Engineering from K. N. Toosi University of Technology. His Master’s thesis focused on the sensitivity analysis of wind turbines using Monte Carlo simulation. His research interests include control system theory, particularly nonlinear systems; system identification and optimization; and renewable energy, especially floating offshore wind turbines.
Research project
- Title: Smart Adaptive Control Scheme for a Multi-Sources Energy System
- Objectives: Development of smart adaptive controllers based on sliding mode theory and artificial intelligence tools; extension to networked control systems; application to a real system, such as a floating offshore wind turbine, via the OpenFAST simulator and Ocean Tanks.
- Expected outcomes: Control algorithms requiring reduced tuning effort and low-computational capability these algorithms will be sufficiently generic to be applied to a large class of systems, including floating offshore wind turbine control and the management of a multi-source energy system.
Main affiliation
Supervisor team
Personal motivation
I am passionate about control systems and renewable energy, particularly in the advancement of floating offshore wind turbines. This PhD allows me to develop adaptive control methods for complex energy systems, contributing to sustainable energy solutions. Working within École Centrale Nantes and the DENSE Doctoral Network, I am eager to collaborate, innovate, and make a meaningful impact in the field of smart control for renewable energy applications.
Personal background
I am a PhD student at École Centrale de Nantes. My project, titled Smart Control and Grid Integration of Floating Wind Turbines, is conducted under the supervision of Prof. F. Plestan, Dr. M. Hamida, and Dr. M.R. Mojallizadeh, who is a member of the LS2N and LAMPA laboratories. My secondment professors are Prof. Reger and Dr. Spanias, who are members of TUIL and EAC. I obtained my master’s degree from Sahand University of Technology with a focus on designing optimal control and estimating uncertainty. My focus areas are optimal control, estimation, and renewable energy, especially floating offshore wind turbines.
Research project
- Title: Smart control and grid integration of floating wind turbines
- Objectives: Control design of floating wind turbines with multi-objectives (power, platform pitch, blade lift force, …); integration to the distribution grid.
- Expected outcomes: New generation of control algorithms requiring a reduced tuning process and including multi-objectives; control design of the power converter; evaluation on a standard simulation software (OpenFAST coupled to Matlab/Simulink).
Main affiliation
Supervisor team
Personal motivation
For me, the main purpose of pursuing a PhD in control engineering is to expand my knowledge and skills in order to conduct high-level research in my favorite fields of interest, such as Optimal Control and Estimation, renewable energy, and floating offshore wind turbines. I aim to develop innovative solutions that enhance efficiency and stability, contributing to a sustainable energy future.
