Javad Mohammadpour Velni

A Fast, Decentralized Covariance Selection-Based Approach to Detect Cyber Attacks in Smart Grids

Recent studies have shown that an attacker can compromise some of the power grid measurements to mislead the conventional state estimators (SEs), since the manipulated measurements can pass the SE residue tests. Statistical structure learning-based approaches have been recently introduced as a powerful tool to detect some of the most complicated cyber attacks. However, the expensive computational complexity of the learning process limits the applicability of these approaches for real time cyber attack detection. This paper proposes a fast and decentralized approach for cyber attack detection based on a maximum likelihood (ML) estimation which exploits the near chordal sparsity of power grids to establish an efficient framework to solve the associated ML estimation problem. The proposed detection method is then decomposed to several local ML estimation problems; this would ensure privacy and reduce the complexity of the underlying problem. The simulation studies validate the efficiency of the proposed method in detecting truly complicated stealthy false data injection attacks.

Coverage control of moving sensor networks with multiple regions of interest

This paper addresses the coverage control problem in environments where several regions of interest exist. To this purpose, a heterogeneous group of robots are deployed to minimize a cost function defined with respect to various spatial probability density functions, each of which describes a desired area for a different group of robots. Each region of interest is assigned to a group of robots with respect to their dynamics and sensing capabilities. A distributed coverage scheme is proposed to allow adjusting to the environment with several important areas in a collaborative way. The regions with higher importance would be covered with an appropriate number of robots. The proposed method also allows for a better allocation of robots to guarantee the desired coverage over the region. Two numerical examples are finally given to examine the proposed coverage approach in case of multiple regions of interest that may need to be covered by a certain number of robots.

A New Voronoi-Based Blanket Coverage Control Method for Moving Sensor Networks

This brief addresses the blanket coverage problem, in which it is desired to cover a long region by moving the blanket within the boundaries representing the main region. To this purpose, a group of autonomous mobile sensors are deployed aiming at maximizing the sensing performance. The blanket coverage area, which is considered to be a region with changing boundaries, is directed to move along the boundaries of the region. Throughout this process, the agents adapt to the varying coverage area by imposing the dynamics of the boundaries on their respective control law. The presented control law ensures that the agents move toward the centroid of their respective Voronoi cell while taking into account the effect of the moving boundaries. The proposed coverage method deploys the agents within the boundaries of coverage area and ensures the (locally) optimal partitioning for the moving coverage area. Performance of the proposed blanket coverage method is examined via numerical examples that use sections of Ohio river and a border buffer zone.

A team-based approach for coverage control of moving sensor networks

The present paper proposes a new team-based approach that allows for forming multiple teams of agents within the coverage control framework. The objective function defined for this purpose tends to minimize the accumulative distance from each agent while reckoning with the given density function that defines the probability of events in the environment to be covered. The proposed team-based approach via the defined optimization problem allows for forming teams of agents when for a variety of reasons, e.g., heterogeneity in their embedded communication capabilities or the dynamics, it is required to keep the similar agents together in the same team. To realize this, the overall objective function is defined as the accumulated sensing cost of individual agents belonging to different teams. The defined collective cost function captures the interdependency of the teams Voronoi cells on the position of the agents that can be viewed as the impact of the dynamic boundaries on the agents. A gradient descent-based controller is designed to ensure the locally optimum configuration of the teams and agents within each team. The convergence of the proposed method is studied to ensure the stability of the implemented controller in both teams and agents final configuration. In addition, a new formation control approach is proposed using the team-based framework to impose either the same or different formation structures while performing the underlying coverage task. It is shown that maintaining the desired configuration through the proposed formation control is achieved at the cost of sacrificing the sensing performance. Finally, the proposed coverage and formation methods are examined via a numerical example where multiple heterogeneous teams of agents with potentially different number of agents are deployed.

An LMI-based approach to distributed model predictive control design for spatially-interconnected systems

This paper proposes a new framework to distributed model predictive control (MPC) design for linear time- and space-invariant (LTSI) distributed systems subject to constraints. Given a two-dimensional, input–output model that describes the distributed dynamics among the subsystems, it is shown that a non-minimal state space realization leads to numerically tractable linear matrix inequality (LMI) based terminal state feedback controller design. The local online optimization problem is defined at the subsystem level with subsystems exchanging predictions through coupled states and can be solved in parallel at all subsystems non-iteratively. Stability and recursive feasibility are guaranteed in the presence of one-step delayed exchanging information among subsystems by imposing consistency constraints and terminal constraints. Attributed to the non-minimal state space realization, input–output properties are preserved in the MPC formulation, and hence no state estimator is needed for the online implementation. Simulation results using a heat equation demonstrate a satisfactory performance of the proposed distributed MPC design compared to centralized MPC schemes.

State-space LPV model identification using kernelized machine learning

This paper presents a nonparametric method for identification of MIMO linear parameter-varying (LPV) models in state-space form. The states are first estimated up to a similarity transformation via a nonlinear canonical correlation analysis (CCA) operating in a reproducing kernel Hilbert space (RKHS). This enables to reconstruct a minimal-dimensional inference between past and future input, output and scheduling variables, making it possible to estimate a state sequence consistent with the data. Once the states are estimated, a least-squares support vector machine (LS-SVM)-based identification scheme is formulated, allowing to capture the dependency structure of the matrices of the estimated state-space model on the scheduling variables without requiring an explicit declaration of these often unknown dependencies; instead, it only requires the selection of nonlinear kernel functions and the tuning of the associated hyper-parameters.

An online LiFePO 4 battery impedance estimation method for grid-tied residential energy storage systems

There has recently been a significant interest directed towards residential battery storage systems mainly motivated by high penetration of renewables, the low cost and high efficiency of power electronic devices, and the advancements in the safety and energy density of the batteries, especially Lithium-Ion (Li-Ion) batteries. Furthermore, the possibility for the end user to become a utility-independent entity with the capacity to overcome power outages and tariff rises is even further propelling this fast growing industry. Lithium iron phosphate (LiFePO4) battery is one of those technologies chosen to take the lead in residential battery storage due to its intrinsic safe performance, good energy density and price. This paper describes an online method for estimating the impedance of LiFePO4 batteries when they are used in residential single phase energy storage systems. Single phase power systems have the intrinsic characteristics of delivering power at twice the frequency of the grid; by energy conservation principle, this pulsating characteristics is transferred directly to the current in the DC stage of the battery storage system. The proposed method takes advantage of this phenomenon and, without interrupting the energy conversion process or adding any external perturbation to the system, is able to characterize, in situ, the AC impedance behavior of the battery. Experimental results are provided to validate the proposed method and simulations show the potential applicability of this method in the assessment of the actual battery aging state.

Cooperative Output Regulation of Multiagent Linear Parameter-Varying Systems

The output regulation problem is examined in this paper for a class of heterogeneous multiagent systems whose dynamics are governed by polytopic linear parameter-varying (LPV) models. The dynamics of the agents are decoupled from each other but the agents’ controllers are assumed to communicate. To design the cooperative LPV controllers, analysis conditions for closed-loop system are first established to ensure stability and reference tracking. Then, the LPV control synthesis problem is addressed, where the offline solution to a time-varying Sylvester equation will be used to determine and update in real time the controller state-space matrices. Two numerical examples will be finally given to demonstrate the efficacy of the proposed cooperative design method.