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Research

1. Renewable & Sustainable Energy and Smart Microgrids

The lab is a center of excellence for the development and optimization of decentralized energy architectures. We focus on creating resilient, carbon-neutral systems that bridge the gap between variable renewable sources and consistent power delivery.

• Smart Microgrid Management: Designing and implementing advanced control strategies for building-integrated microgrids. Our research focuses on the seamless integration of wind, solar, and battery energy storage systems (BESS) to ensure grid stability and peak shaving in smart city environments.

• Renewable Energy Conversion: Optimizing the performance of wind turbines and photovoltaic systems through high-efficiency power electronics and maximum power point tracking (MPPT) algorithms.

• Intelligent Energy Management Systems (EMS): Developing AI-driven supervisory controllers that utilize real-time data to manage energy flow, reduce operational costs, and maximize the utilization of sustainable resources.

• Hybrid Energy Systems: Investigating the synergy between different energy carriers—such as the integration of hydrogen storage with electrical microgrids—to enhance long-term energy security and sustainability.

2. Circular Economy & Waste-to-Resource Transformation

The lab is dedicated to advancing the circular economy by engineering processes that convert diverse waste streams into high-value commodities and clean energy.

• Waste Valorization via Pyrolysis: Developing thermochemical pathways to transform varied waste materials into biochar, bio-oil, and synthetic gas.

• Biochar & Solid Recovered Materials: Investigating the applications of solid carbon products for soil enhancement, carbon sequestration, and as sustainable additives in industrial materials.

• Pyrolysis Oil & Recycling: Researching the recovery of energy from waste sources to replace fossil-based feedstocks in the energy and manufacturing sectors.

3. Machine Learning & Deep Learning for Predictive Modeling

We leverage advanced Artificial Intelligence (AI) architectures to enhance the intelligence, efficiency, and reliability of complex engineering systems. By integrating data-driven approaches with traditional control theory, the lab develops high-performance models for real-time process monitoring and system optimization.

• Predictive Analytics: Utilizing Machine Learning (ML) models to analyze historical data and provide real-time parameter prediction for thermal, mechanical, and electrical processes.

• Deep Learning (DL) Architectures: Implementing sophisticated neural networks to handle high-dimensional sensor data and complex time-series forecasting.

• Intelligent Process Monitoring: Developing robust algorithms for anomaly detection and fault diagnosis, ensuring the safety and operational continuity of industrial automation systems.

• Hybrid Modeling: Combining physics-based models with deep learning to create "Digital Twins" that accurately simulate and predict the behavior of renewable energy systems and industrial processes.

4. Advanced Mechatronics & Automation

The S³A Lab conducts research in the design and implementation of intelligent mechanical systems, bridging the gap between physical hardware and high-level computational control.

• Electric Drives & Mobility: Research into control architectures for electric mobility and high-performance electric motors, focusing on the optimization of torque control and power management.
• Robotics & Autonomous Systems: Development of control algorithms for industrial robotic arms and mobile platforms. Research areas include path planning, collaborative robotics, and the integration of machine vision for automated assembly, inspection, and material handling.

• IoT & Precision Systems: Design of IoT-integrated sensor networks and communication protocols for real-time monitoring. These systems are applied in various fields, including industrial management, precision farming, and environmental monitoring.

• Industrial Automation & Cyber-Physical Systems: Implementation of automation solutions that integrate physical processes with digital control, utilizing platforms for hardware-in-the-loop (HIL) testing and rapid prototyping.

Projects

Robotics for Retail Applications

In this project, a robot is investigated and built to conduct tasks, in retail environments, such as checking the barcodes and detecting the products availability. Different technologies are used such as

1. )
4. GPU for ROS engine - and 
5. Cameras - &
6. Raspberry PI technologies

Power Prediction and Management of Distributed Solar PV with Battery Storage System

 

Study and Analysis of Industrial Islanded Solar PV Microgrid

An industrial islanded solar PV microgrid, based on Schneider technology, is analysed through studying the behavior of all microgrid components to ensure power balance in the system.

Grid-linked Renewable (Wind, solar) Energy System with Fault Ride Through (FRT) Capability

The integration of wind turbine and photovoltaics into distribution power systems with grid fault ride-through capability is investigated by proposing robust control schemes based on model based control strategies such as predictive control, sliding mode control and feedback linearizing control. In normal grid conditions, the control system regulates the constant dc-link voltage, and it also provides the maximum power transfer to the grid by controlling unity power factor. During grid abnormality, the control system is reorganized to maintain a constant dc-link voltage, prevents the active power and injects a reactive power to support the grid operation. Robustness and efficieciency of the control systems are verified and experimentally validated using a hardware prototype of the renewable energy system connected to an emulated three-phase grid.

Control system under normal grid conditions

Control system under grid fault

Power Management and Control of Microgrid Based Renewable Energy Sources

Experimental microgrid based renewable (wind, solar) energy generators with battery storage are developed using Festo (LabVolt) modules driven by the OPAL-RT HIL Real-Time Digital Simulator (OP5600). Control and power management systems are designed to supervise the hybrid energy system by producing appropriate power from the distributed generators. Based on the load demand and the produced power, a supervisory control system regulate the charge-discharge process of the battery bank based on the power demand and generation.

Different control strategies (predictive, feedback linearization, sliding mode, ....) are investigated for controlling various electric drives (permanent magnet synchronous, induction, DC, doubly-fed induction). State estimation schemes, using conventional and artificial intelligence methods, are developed for sensorless operation.

 

Condition Monitoring and Fault Diagnosis

Detection of fault conditions based on measurements of vibration made on rotary machines and sensors' measurements in various systems is investigated to identify significant changes indicating faults. Different statistical and artificial intelligence tools will be used in the development of fault detection systems. 

Development of Power Electronics, Measurement Circuits, and Monitoring Interface for Renewable Energy Experimental Systems

Power electronics and measurement circuits, with monitoring interface, are developed to operate experimental wind and photovoltaic energy systems using different technologies. NI Single-Board RIO General-Purpose Inverter Controller and NI CompactRIO controllers are used to develop the control system with monitoring through LabVIEW interface. Furthermore, Quanser technologies are used in control development using Quarc software. The hybrid renewable energy system is operated through a power management system including control and monitoring.