Research

Research Interests:

Current Projects:

(DoD-ONR) Deep Learning-based Control for Naval Power and Energy Systems, 2023-2028 ($970,000), Lead PI, Department of Defense, Office of Naval Research (ONR).

(DoD-ONR) Real-time Voltage/Frequency Indexes for Assessing the Real-time Resilience of Navy Microgrids, 2023-2026 ($547,000), Lead PI, Department of Defense, Office of Naval Research (ONR).

(NSF-EPCN) Data-Driven Dynamic State-estimation for Modern Power Systems, 2022-2025 ($431,000) Lead PI, National Science Foundation, Energy, Power, Control, and Networks (EPCN).

  (Pennsylvania Department of Community and Economic Development (DCED) ) Grid Integration of Distributed Energy Resources using DC Interlinks 2023-2024 ($94,000), Lead PI, Pennsylvania Infrastructure Technology Alliance (PITA)

 (Pennsylvania Department of Community and Economic Development (DCED) ) Contingency Analysis and Optimal Microgrid Integration to Quakertown Grid 2022-2023 ($99,000), Lead PI, Pennsylvania Infrastructure Technology Alliance (PITA)

(Pennsylvania Department of Community and Economic Development (DCED) Grid-interactive Smart Building Energy Management through Thermal Storage and Electric Vehicles,  2023-2024 ($93,000), Co-PI, Pennsylvania Infrastructure Technology Alliance (PITA)

 (Pennsylvania Department of Community and Economic Development (DCED) ) Cybersecurity for Interdependent Systems in Building Management, 2022-2023 ($89,903), Co-PI, Pennsylvania Infrastructure Technology Alliance (PITA)

 (Internal Grant-Lehigh University ) AI-based Cyberattack Detection for Smart Water Distribution Systems, 2023-2024 ($30,000), Co-PI, Lehigh's Faculty Innovation Grant (FIG).

Past Projects:

(Internal Grant-Lehigh University ) Machine Learning-based Grid Optimization, 2022 ($6000), PI, Lehigh's Faculty Research Grant (FRG).

 (Internal Grant-Lehigh University ) Predictive Control of Interdependent Water-Energy Systems, 2022 ($6000), Co-PI, Lehigh's Faculty Research Grant (FRG)

(Internal Grant-Penn State University ) Application of Big Data Analytics in Detection of Cyberattacks in Smart Energy-Water Networks, 2020-2021 ($94,000), PI, Penn State's Center for Security Research and Education (CSRE)

  (DoD-ONR) Distributed Control of Distributed Energy Resources and Cybersecurity Testbed, 2020 ($181,000), PI, Department of Defense, Office of Naval Research (ONR). 

 (Internal Grant-Penn State University ) Application of Advanced Math for Cybersecurity Modeling and Detection of False Data Injection in Power Systems, 2019-2020  ($15,000), PI, Penn State's Center for Security Research and Education (CSRE).

(Internal Grant-Penn State University ) Development of a Gallium Nitride (GaN) Transformer for Solar Intermittency Compensation, 2019-2020  ($40,000), Co-PI, Penn State's Materials Research Institute (MRI).

 (Internal Grant-Penn State University ) Hardware Implementation of Modular Multi-level Converters for Solar Applications, 2018-2019  ($25,000), PI, Penn State's Institute for Energy and Environment (IEE).

 (Internal Grant-Penn State University ) Distributed Control of Energy Storage Devices in Smart Grids, 2017-2018 ($7,500), PI, Penn State Harrisburg's SEED grant.

(DoD-ONR) Plug and Play 45 kW Microgrid Testbed at Penn State Harrisburg, 2016-2019 ($275,000), Co-PI, Department of Defense, Office of Naval Research (ONR).

Current Research:

1- Machine Learning for Cybersecurity of Smart Grids

I work on smart grid resilience against cyberattacks by exploring novel modeling frameworks and detection methods for stealthy false data injection attacks. These attacks are normally targeted and are aimed at manipulating the advanced metering infrastructure (AMI) readings to bypass the bad data detection algorithms used in AC and DC state estimation methods. I am interested in exploring novel statistical machine learning and signal processing approaches for grid resilience. 

2- Data-Driven Optimization and Control for Energy Systems

I work on solving the optimization and control challenges in inverter-based smart grids with data-driven and machine learning techniques. I am interested in exploring data-driven physics-based system identification techniques and novel control approaches (classical and advanced) applied to distributed energy resources. 

3- Distributed Control in Cyber-Physical Systems

I work on designing distributed controllers for cyber-physical systems (mainly smart grids) and their components such as energy storage, photo-voltaic (PV) or wind farms. I am looking for advanced secondary control designs (data-driven and physics-based) for control of DERs in smart grids and microgrids.


4- Experimentation in Cyber-Physical Power Systems

In this research area, I focus on real-time and hardware in the loop (HIL) experimentation of power electronics converters with various control designs for grid-forming and grid-following mode of operation. Our laboratory has the capability for real-time simulations, hardware-in-the-loop tests of DERs with up to 10kW capacity, and power electronics-based control design. 


6- Control and Security for Grid Interdependent Infrastructures

In this research area, I explore novel techniques for resource allocation and cybersecurity of resources in interdependent infrastructures such as water-energy, water-energy-building, or water-energy-transportation systems. We look for novel optimization techniques and machine learning approaches for real-time operation of interdependent infrastructures.