RESEARCH

InterCONnected Critical Infrastructure Systems

Engineering (CONCISE) Laboratory

Ongoing Research:

4- Dynamic State Estimation of Smart Water Distribution Systems with Low Observability

3- Data-driven control of cyber-physical infrastructure systems

2- Cybersecurity of Critical Interdependent Systems (Water-energy)

1- Model Predictive Control of Critical Interdependent Systems (Water-energy)

Hardware-in-the-loop Testbeds:

Scaled-down smart water distribution system (manufactured by Bitlismen) coupled with an Opal-rt real-time simulator

This testbed consists of a reservoir, a storage tank, 5 controllable pumps, 2 solenoid, 2 analog and 8 manual valves, 4 pressure sensors, 3 flow meters, 1 ultrasonic digital level sensor, 4 VFDs, a myRIO controller, a LabView-based supervisory control and data acquisition (SCADA) system, and an Opal-RT real-time simulator. Opal-RT is equipped with a simulation environment, named RT-lab, which can communicate between MATLAB-Simulink and LabView. RT-lab will allow us to integrate a simulated WDS (in MATLAB) into our physical testbed via I/O connections and create a HIL setup for validation.

2. Quadruple tank system with a real-time simulation interface (manufactured by Bitlismen)

Our quadruple tank system is a complex, nonlinear, and multiple-input multiple-output system that can operate under both minimum and non-minimum phase conditions, making it a versatile testing ground for evaluating the robustness of control strategies.

Previous Projects:

Modeling integrated water-energy systems to optimize water-energy nexus

I focus on optimization of 1) demand response using water management, 2) combined water and energy systems, and 3) building's energy management system integrated with water-energy systems to develop a smart and sustainable city in the future. I develop mathematical optimization models that minimize the energy consumption of water-energy systems by developing single-objective, multi-level, and co-optimization models using mixed integer nonlinear programming (MINLP) formulation. Via co-optimization models, I minimize the energy consumption of the pumps in water distribution systems (WDS) considering the uncertain behavior of renewable sources including solar or wind energy generation units. I contemplated an integer variable for the status of each pump which takes in 0 and 1 depending on the amount of water supplied to the network, the amount available in the tank, and the demand at every hour. Additionally, to obtain a global optimum solution for the proposed non-linear programming formulations, I develop convex optimization models using bivariate and univariate piecewise linear approximation to linearize the nonlinear constraints.

Figure 1. Water-energy nexus optimization.

Water Systems Security and Resilience

My research is focused on two important aspects of cybersecurity in water distribution systems: (1) Developing models and test cases to study various types of cyberattacks in water distribution networks, and (2) detection of cyberattacks that are deliberately designed to fail components of water distribution network or cause cascading failures. In the first aspect, I focus on designing and analyzing stealthy false data injection (FDI) cyberattack models that can bypass bad data detection algorithms to help authorities develop more practical, precise and timely countermeasures. So far, I have focused on modeling stealthy cyberattack in water distribution systems that can cause 1) nodal head increase and cascading failures, and 2) tanks' overflown or withdrawn. The modeling uses a bi-level nonlinear programming formulation that accounts for false data injections that could successfully bypass state-estimation algorithm without being detected by the operator. The research results are currently in-review for publication in the Journal of Hydrology and Sustainable Cities and Societies.

In a separate effort to detect cyberattacks in water distribution networks, I develop optimization models using MINLP formulation that computes nodal demands and pressure heads in WDS to be compared with those obtained from the state-estimation algorithm, identifying potential FDI attacks. In another research, I work on developing an integrated state-estimation framework to detect potential FDIs in a combined water-energy system (shown in the figure below).

This research was supported by funding from Center for Security Research and Education to use machine learning algorithms to detect cyberattacks in water and energy networks.