High penetration of wind energy is pushing wind farms (WFs) to offer grid support capabilities, such as active power tracking. One of the main challenges in active power tracking for WFs is the interaction of wind turbines (WTs) through their wakes. This reduces the available wind in downstream WTs, leading them to saturation, while also affecting structural loading. With the increasing number of WTs in individual WFs, the computational and communication complexity of implementing centralized control architectures grows, posing challenges for real-world applications. In this article, we present a novel distributed control approach for active power tracking for WFs, namely multirate consensus-based distributed control (MCDC). The MCDC is designed to ensure that tracking errors caused by WT saturation are equally compensated throughout the WF, while only requiring local information exchanges between WTs. Furthermore, the proposed controller ensures that WT aerodynamic loading is balanced across the WF in a distributed manner. Finally, the overall power reference is distributed via a leader–follower consensus algorithm, resulting in a fully distributed approach. Our control approach facilitates the WF modularity and sparsity, which reduces the costs associated with control design and its applicability. Throughout this article, we demonstrate the effectiveness of the proposed MCDC through high-fidelity simulations, presenting performance comparable to the centralized control.

Developing accurate models for batteries, capturing ageing effects and nonlinear behaviors, is critical for the development of efficient and effective performance. Due to the inherent difficulties in developing physics-based models, data-driven techniques have been gaining popularity. However, most machine learning methods are black boxes, lacking interpretability and requiring large amounts of labeled data. In this paper, we propose a physics-informed encoder–decoder model that learns from unlabeled data to separate slow-changing battery states, such as state of charge (SOC) and state of health (SOH), from fast transient responses, thereby increasing interpretability compared to conventional methods. By integrating physics-informed loss functions and modified architectures, we map the encoder output to quantifiable battery states, without needing explicit SOC and SOH labels. Our proposed approach is validated on a lithium-ion battery ageing dataset capturing dynamic discharge profiles that aim to mimic electric vehicle driving profiles. The model is trained and validated on sparse intermittent cycles (6 %–7 % of all cycles), accurately estimating SOC and SOH while providing accurate multistep ahead voltage predictions across single and multiple-cell based training scenarios.

In this paper, we address the problem of secure estimation in networked systems, by focusing on false data injection attacks in large-scale systems, where malicious attackers alter the original transmitted data between subsystems. We propose a technique that ensures asymptotic secure estimation of the original transmitted data under two attack classes, termed stealthy and non-stealthy, while also providing detection and isolation capabilities. We give conditions under which asymptotic recovery of nominal performance is guaranteed, thus providing the large-scale system with resilience. Furthermore, we demonstrate the effectiveness of the proposed technique through a simulation-based case study.

This paper examines the properties of output-redundant systems, that is, systems possessing a larger number of outputs than inputs, through the lense of the geometric approach of Wonham et al. We begin by formulating a simple output allocation synthesis problem, which involves “concealing” input information from a malicious eavesdropper having access to the system output, while still allowing for a legitimate user to reconstruct it. It is shown that the solvability of this problem requires the availability of a redundant set of outputs. This very problem is instrumental to unveiling the fundamental geometric properties of output-redundant systems, which form the basis for our subsequent constructions and results. As a direct application, we demonstrate how output allocation can be employed to effectively protect the input information from certain output eavesdroppers with guaranteed results.

Over-actuated systems, namely systems with more inputs than outputs, can increase control performance, yet are susceptible to model-based undetectable attacks if the actuator channel is compromised. In this paper, we show how implementing a sparse actuator schedule can introduce security by rendering these attacks ineffective. We formulate a novel methodology whereby a periodic sparse schedule, implemented at the actuators, secures the system by ensuring that a malicious adversary cannot exploit actuator redundancy to deploy undetectable attacks. The schedule is designed offline and repeats periodically at the actuators, so that the adversary is constrained to only tamper with the active actuators. We devise a degeneracyaware greedy selection procedure with low computational complexity to design an actuator schedule that renders undetectable attacks ineffective, whilst simultaneously providing relatively small performance degradation. We illustrate the effectiveness of our approach using a reference tracking model predictive controller on the IEEE-39 bus power network employing the designed sparse schedule.

Wind turbines degrade over time, resulting in varying structural, aeroelastic, and aerodynamic properties. In contrast, the turbine controller calibrations generally remain constant, leading to suboptimal performance and potential stability issues. The calibration of wind turbine controller parameters is therefore of high interest. To this end, several adaptive control schemes based on extremum seeking control (ESC) have been proposed in the literature. These schemes have been successfully employed to maximize turbine power capture by optimization of the Kω²-type torque controller. In practice, ESC is performed using electrical generator power, which is easily obtained. This paper analyses the feasibility of torque gain optimization using aerodynamic and generator powers. It is shown that, unlike aerodynamic power, using the generator power objective limits the dither frequency to lower values, reducing the convergence rate unless the phase of the demodulation ESC signal is properly adjusted. A frequency-domain analysis of both systems shows distinct phase behavior, impacting ESC performance. A solution is proposed by constructing an objective measure based on an estimate of the aerodynamic power.

Multiplicative watermarking (MWM) is an active diagnosis technique for the detection of highly sophisticated attacks, but is vulnerable to malicious agents that use eaves-dropped data to identify and then remove or replicate the watermark. In this work, we propose a scheme to protect the parameters of MWM, by proposing a design strategy based on piecewise affine (PWA) hybrid dynamical systems, called hybrid multiplicative watermarking (HMWM). Due to the design decision to make certain states of the HMWM systems unobservable, we show that parameter reconstruction by an eavesdropper is infeasible, from both a computational and a system-theoretic perspective, while not altering the system's closed-loop performance.

In this paper we present a novel switching function for multiplicative watermarking systems. The switching function is based on the algebraic structure of elliptic curves over finite fields. The resulting function allows for both watermarking generator and remover to define appropriate system parameters, sharing only limited information, namely a private key. We prove that the resulting watermarking parameters lead to a stable watermarking scheme.

With increasing installations of grid-connected power electronic converters in the distribution network, there is a new trend of using distributed control in a cyber layer to coordinate the operations of these power converters for improving power system stability. However, cyber-attacks remain a threat to such distributed control. This paper addresses the cyber-attack detection and a countermeasure of distributed electric springs (ESs) that have emerged as a fast demand-response technology. A fully distributed model-based architecture for cyber-attack detection in the communication network is developed. Based on a dynamic model of ES with consensus control, a local state estimator is proposed and practically implemented to monitor the system. The estimator is fully distributed because only local and neighboring information is necessary. A countermeasure for the distributed ESs to ride through the cyber-attack and maintain regulatory services in a microgrid is demonstrated successfully. Experimental results are provided to verify the effectiveness of the proposed cyber-attack detection method and its ride-through capability.