Electrical Engineering And Systems Science

This paper proposes a distributed cyber-attack detection method in communication channels for a class of discrete, nonlinear, heterogeneous, multi-agent systems that are controlled by our proposed formation-based controller. A residual-based detection system, exploiting a neural network (NN)-based observer, is developed to detect false data injection attacks on agents communication channels. A Lyapunov function is used to derive the NN weights tuning law and the attack detectability threshold. The uniform ultimate boundedness (UUB) of the detector residual and formation error is proven based on the Lyapunov stability theory. The proposed methods attack detectability properties are analyzed, and simulation results demonstrate the proposed detection methodologys performance.

Queueing-network models are developed for enhanced and next-generation 911 (E911 and NG911) systems, which capture both their cyber-components (communications, data-processing) and physical-world-elements (call takers, vehicle dispatch). The models encompass both the call-processing and dispatch functions of 911, and can represent the interdependencies between multiple PSAPs enabled by NG911. An instantiation of the model for a future NG911 system for Charlotte, North Carolina, is developed and used to assess performance metrics. Representation of cyber-threats (e.g. Distributed Denial-of-Service attacks) within the queueing-network model is undertaken. Based on these representations, the model is used for analysis of holistic threat impacts, as a step toward risk and vulnerability assessment for future emergency response systems.

Reliability and voltage quality in distribution networks have been achieved via a combination of transformer power rating satisfaction and voltage management asset control. To maintain reliable operation under this paradigm, however, future grids with deep DER penetrations would require costly equipment upgrades. These upgrades can be mitigated via judicious coordination of DER operation. Earlier work has assumed a hierarchical control architecture in which a global controller (GC) uses detailed power injection and DER data and knowledge of DER owners' objectives to determine setpoints that local controllers should follow in order to achieve reliable and cost effective grid operation. Having such data and assuming knowledge of DER owners' objectives, however, are often not desirable or possible. In an earlier work, a 2-layer DER coordination architecture was shown to achieve close to optimal performance despite infrequent communication to a global controller. Motivated by this work, this paper proposes a day-ahead coordination scheme that uses forecasted power profile ranges to generate day-ahead dynamic power rating bounds at each transformer. Novel features of this scheme include: (i) the GC knows only past node power injection data and does not impose or know DER owner objectives, (ii) we use bounds that ensure reliable operation to guide the local controllers rather than setpoint tracking, and (iii) we consider electric vehicle (EV) charging in addition to storage. Simulations using the IEEE 123-bus network show that with 50% solar, 50% EVs and 10% storage penetrations, the uncoordinated approach incurs rating violations at nearly all 86 transformers and results in 10 times higher voltage deviation, while our approach incurs only 12 rating violations and maintains almost the same voltage deviations as before the addition of solar and EVs.

Given one open-loop measured trajectory of a single-input single-output discrete-time linear time-invariant system, we present a framework for data-driven controller design for closed-loop finite-horizon dissipativity. First, we parametrize all closed-loop trajectories using the given data of the plant and a model of the controller. We then provide an approach to validate the controller by verifying closed-loop dissipativity in the standard feedback loop based on this parametrization. We use these conditions to design controllers leading to closed-loop dissipativity based on a quadratic matrix inequality feasibility problem. Finally, the results are illustrated with a simulation example.

With the large-scale integration of renewable power generation, frequency regulation resources (FRRs) are required to have larger capacities and faster ramp rates, which increases the cost of the frequency regulation ancillary service. Therefore, it is necessary to consider the frequency regulation cost and constraint along with real-time economic dispatch (RTED). In this paper, a data-driven distributionally robust optimization (DRO) method for RTED considering automatic generation control (AGC) is proposed. First, a Copula-based AGC signal model is developed to reflect the correlations among the AGC signal, load power and renewable generation variations. Secondly, samples of the AGC signal are taken from its conditional probability distribution under the forecasted load power and renewable generation variations. Thirdly, a distributionally robust RTED model considering the frequency regulation cost and constraint is built and transformed into a linear programming problem by leveraging the Wasserstein metric-based DRO technique. Simulation results show that the proposed method can reduce the total cost of power generation and frequency regulation.

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.

A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.

Unlike fixed-gain robust control, which trades off performance with modeling uncertainty, direct adaptive control uses partial modeling information for online tuning. The present paper combines retrospective cost adaptive control (RCAC), a direct adaptive control technique for sampled-data systems, with online system identification based on recursive least squares (RLS) with variable-rate forgetting (VRF). The combination of RCAC and RLS-VRF constitutes data-driven RCAC (DDRCAC), where the online system identification is used to construct the target model, which defines the retrospective performance variable. This paper investigates the ability of RLS-VRF to provide the modeling information needed for the target model, especially nonminimum-phase (NMP) zeros. DDRCAC is applied to single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) numerical examples with unknown NMP zeros, as well as several flight control problems, namely, unknown transition from minimum-phase to NMP lateral dynamics, flexible modes, flutter, and nonlinear planar missile dynamics.

In this paper, we present a data-driven secondary controller for regulating to some desired values several variables of interest in a power system, namely, electrical frequency, voltage magnitudes at critical buses, and active power flows through critical lines. The power generation system is based on distributed energy resources (DERs) interfaced with either grid-forming (GFM) or grid-following (GFL) inverters. The secondary controller is based on online feedback optimization leveraging the learned sensitivities of the changes in the system frequency, voltage magnitudes at critical buses, and active power flows through critical lines to the changes in inverter active and reactive power setpoints. To learn the sensitivities accurately from data, the feedback optimization has a built-in mechanism for keeping the secondary control inputs persistently exciting without degrading its performance. The feedback optimization also utilizes the learned power-voltage characteristics of photovoltaic (PV) arrays to compute DC-link voltage setpoints so as to allow the PV arrays to track the power setpoints. To learn the power-voltage characteristics, we separately execute a data-driven approach that fits a concave polynomial to the collected power-voltage measurements by solving a sum-of-squares (SoS) optimization. We showcase the secondary controller using the modified IEEE-14 bus test system, in which conventional energy sources are replaced with inverter-interfaced DERs.

We propose a method to perform set-based state estimation of an unknown dynamical system using a data-driven set propagation function. Our method comes with set-containment guarantees, making it applicable to the estimation of safety-critical systems. The method consists of two phases: (1) an offline learning phase where we collect noisy state-input data to determine a function to propagate the state-set ahead in time; and (2) an online estimation phase consisting of a time update and a measurement update. It is assumed that sets bound measurement noise and disturbances, but we assume no knowledge of their statistical properties. These sets are described using zonotopes, allowing efficient propagation and intersection operations. We propose two approaches to perform the measurement update. The method is extended to constrained zonotopes. Simulations show that the proposed estimator yields state sets comparable in volume to the confidence bounds obtained by a Kalman filter approach, but with the addition of state set-containment guarantees. We observe that using constrained zonotopes yields smaller sets, but with higher computational cost compared to unconstrained zonotopes.