In direct-current (DC) microgrids (MGs), distributed secondary control is essential for achieving both voltage restoration and accurate current sharing, wherein precise parameter estimation plays a critical role in ensuring satisfactory control performance. To address system uncertainties, this paper presents a cascaded framework consisting of an adaptive parameter estimator and a distributed secondary controller for DC MGs. The proposed framework enables real-time online estimation of transmission line resistance and inductance while fulfilling the voltage restoration and current sharing. Since it is often challenging to validate the persistent excitation (PE) condition in practical DC MGs, we propose two parameter estimation algorithms. The first one is a standard gradient descent estimator, which requires strict PE to be satisfied. The second algorithm integrates preconditioning dynamics with gradient descent and only needs a weaker interval excitation (IE) condition to achieve effective estimation. Moreover, by integrating a dynamic average model with the virtual current derivative (VCD) approach, the DC MG system is reduced to a second-order model, which simplifies the joint design of parameter estimation and control, as well as facilitates closed-loop stability analysis. Finally, the effectiveness of the proposed method is verified through numerical simulations and experiments.

In this paper, the optimal power allocation and frequency restoration problem for islanded alternating-current (AC) microgrids (MGs) is addressed under false data injection (FDI) frequency sensor attacks. Firstly, a distributed proportional and integral secondary controller is developed without considering the FDI attack. In addition to the advantages of the traditional distributed structure, this controller also has more advantages, such as simple tuning and robustness to external disturbances. Also, the theoretical analysis proves that the developed controller can drive the system frequency to the reference value, while the optimal power distribution is realized. Then, a distributed intermediate-estimate-based resilient secondary control strategy is further designed to enhance the resilience of cyber-physical AC MG to FDI attacks on frequency sensors. Compared to most of existing resilient control strategies, our proposed controller can not only maintain the frequency stability, but also ensure the accuracy of optimal power sharing among distributed generators (DGs). Finally, an islanded AC MG test system is built on a real-time testing platform to illustrate and verify the effectiveness of the developed methods.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g., overshoot, convergence time, steady-state accuracy, and maximum deviation) at both kinematic and kinetic levels, FSQDs are designed by transforming constrained AUV model into an unconstrained model via one-sided hyperbolic cosecant boundaries and nonlinear mapping functions. An intermittent sampling-based neural estimator (ISNE) is devised to reconstruct the matched and mismatched lumped disturbances as well as immeasurable velocity states of transformed AUV model, where only system outputs after intermittent sampling are required. Using the estimations of ISNE and the system outputs after triggering, an intermittent output feedback control law incorporated with hybrid threshold event-triggered mechanism (HTETM) is designed to achieve ultimately uniformly bounded (UUB) results. Simulation results are provided and analyzed to validate the effectiveness of the studied control strategy with application to an omnidirectional intelligent navigator (ODIN).

Due to the widespread adoption of distributed generators and the increasing diversity of loads, AC microgrid has emerged as a prominent research area. However, there is a dearth of research outcomes that address the achievement of precise AC bus voltage/frequency restoration and active/reactive power sharing control objectives within a predefined time. In this paper, a distributed predefined-time secondary controller is presented to fulfill the aforementioned objectives without load power, which is accomplished by defining composite errors and employing a distributed predefined-time observer. Notably, the convergence time of bus voltage and the output power of converters can be tailored by a predefined parameter. Numerical simulation tests have been conducted to verify the efficacy of the predefined-time method in scenarios involving load variations and plug-and-play.

High-precision clock synchronization is required for many industrial Internet of Things (IIoTs) supporting real-time monitoring and control for applications, such as the smart grid. For high-precision clock synchronization in IIoTs, several approaches can be used, such as the IEEE 1588 protocol, the Network Time Protocol (NTP), and the Global Navigation Satellite System (GNSS) based methods. Among them, the GNSS-based methods are more appropriate for IIoTs distributed in wide areas. It has been reported that nanosecond-level time synchronization can be achieved by GNSS receivers. However, the end-to-end accuracy of the system clocks in the application processor is a different story. In this study, an experimental testbed has been built using the open-source Linux system, the BeiDou receiver, and the open-source Raspberry Pi 4 hardware. The Pulse-Per-Second (PPS) signals from the BeiDou receiver are used as the reference to synchronize the system clocks. With a one-second synchronization interval, experiments indicate that the system clock achieves an accuracy of 1 µs with a success rate of 92.7% and 2 µs with a success rate of 99.7%. While increasing the synchronization interval, the system clock relies more on the local crystal oscillator, and the timing error increases to 100 µs in 500 seconds.

In this article, under directed graphs, an adaptive consensus tracking control scheme is proposed for a class of nonlinear multiagent systems with completely unknown control coefficients. Unlike the existing results, here, each agent is allowed to have multiple unknown nonidentical control directions, and continuous communication between neighboring agents is not needed. For each agent, we design a group of novel Nussbaum functions and construct a monotonously increasing sequence in which the effects of our Nussbaum functions reinforce rather than counteract each other. With these efforts, the obstacle caused by the unknown control directions is successfully circumvented. Moreover, an event-triggering mechanism is introduced to determine the time instants for communication, which considerably reduces the communication burden. It is shown that all closed-loop signals are globally uniformly bounded and the tracking errors can converge to an arbitrarily small residual set. Simulation results illustrate the effectiveness of the proposed scheme.

A wide range of cyber–physical systems can be modeled as Euler–Lagrange dynamics, whose inherent nonlinearities bring additional difficulties in the design and analysis of controllers for such systems. The objective of this article is to solve the jamming-resilient synchronization control problem of networked Lagrangian systems subject to unknown external disturbances, with the quantized sampling data. Under a jamming attack, a normal communication channel will be blocked with unknown frequencies and intervals. To achieve jamming attack resilience, a robust adaptive controller is proposed. Then a novel auxiliary system is designed for each subsystem over a directed network. By using the quantized sampling data from each subsystem and its neighbors, the jamming attack can be handled by an embedded single-integral subsystem. Sufficient conditions that are independent of the attack parameters are derived to guarantee the global stability of the closed-loop system. Compared with existing jamming-resilient methods, the proposed approach is concise and resilient to any jamming attacks with bounded frequencies and durations.

Advances in information and communication technologies have significantly influenced the operation of low-voltage distribution grids. As essential elements of distribution grids, user-side smart meters find many smart grid applications, for example to measure electrical energy use and facilitate communications. However, the service models of distribution grids remain under development in association with upgrading of user-side smart meters. These meters are resource constrained, and challenging to upgrade on a large scale. To address this issue, this article describes a container-driven service architecture, in which containers are used to create a virtual dedicated agent (digital twin) for each user-side smart meter. The agent can be deployed either in the cloud or on an edge system, and can be upgraded to support emerging smart grid applications, thus minimizing the future upgrading requirements of user-side smart meters. We built experimental test beds to verify the proposed architecture and evaluated its performance in real-world experiments.

This article tries to provide a new alternative approach to solve the economic dispatch (ED) problem in a smart grid system. Such a problem has been widely studied recently with several advanced numerical optimization algorithms being proposed. However, most of these numerical algorithms may suffer from high computational cost for on-line optimization. In this article, we aim to address this problem by proposing a learning-based optimization strategy. The key idea is to regard the optimization strategy of the ED problem as an unknown mapping relationship. With the help of traditional ED optimization algorithms to obtain the ground truth, we employ a deep neural network (DNN) to learn the ED optimization strategy and use it for online ED. In particular, our main contribution in this article is to theoretically show that one popular ED algorithm, i.e.,$\lambda $-iteration algorithm, can be accurately approximated by a well-constructed DNN with finite network size. Moreover, dynamic units status of dispatchable generators is also considered and can be well solved by our proposed approach. Furthermore, several simulation case studies implemented on a 3-unit power system and an IEEE-30 bus power system validate the effectiveness of our proposed method.

In this paper, we study the adaptive tracking control problem for a class of uncertain nonlinear systems with input quantization. Different from the existing results, we propose a new quantizer with saturated quantization levels motivated by the saturation property of practical actuators and sensors. With this new quantizer, we know the exact number and values of the quantization levels in advance, regardless of the magnitude of the designed control signal. Thus, we only need to code these quantization levels accordingly such that less network resources are consumed. It is shown that the proposed control scheme guarantees that all the closed-loop signals are globally bounded and the tracking error converges towards a known compact set.

This paper considers the distributed economic dispatch problem in power systems. A novel constrained consensus-based iterative algorithm is proposed to cooperatively search the optimal incremental cost. The algorithm is carried out by alternately executing a continuous-time finite-time consensus algorithm and employing local projection operations only when consensus variables reach agreement. At each iteration, the initial values of consensus variables are restored locally based on the deviation between the consensus value in the last iteration and its projection on local constraints. Compared to existing methods, our method only needs a single consensus algorithm and does not require continuous projection operations. Thus each node in our algorithm only transmits a single variable to neighbours and the search time of the optimal solution is significantly reduced. Convergence analysis is given, and numerical examples are presented to show the effectiveness of our method with comparison to some existing results.

Modern power grid is increasingly integrated with battery energy storage systems (BESSs). This paper deals with the problem of state-of-charge (SoC) balance control for multiple distributed BESSs in smart grid. The BESSs are expected to work cooperatively to not only fulfil the overall power requirement but also meet the constraints of the same relative SoC variation rate. To achieve this objective, a distributed SoC balance control approach is presented with event-triggered signal transmissions. It is designed with the dynamic average consensus (DAC) mechanism for parameter estimations. The DAC enables distributed control of each BESS through communicating with its neighboring BESSs. Different from traditional periodic signal transmission, the event-triggered signal transmission embedded in our approach allows each BESS to transmit signal to its neighboring BESSs only when needed, thus reducing the communication traffic. Theoretical lower bounds are established for consecutive interevent intervals such that the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of the presented approach.

In this paper, we propose a new distributed event-trigger consensus protocol for linear multiagent systems with external disturbances. Two consensus problems are considered: one is a leader-follower case and the other is a nonleader case. Different from the existing results, our proposed scheme enables each agent to decide when to transmit its state signals to its neighbors such that continuous communication between neighboring agents is avoided. Clearly, this can largely decrease the communication burden of the whole communication network. Besides, since the control signal for each agent is discontinuous because of the event-triggering mechanism, the existence of a solution for the closed-loop system in the classical sense may not be guaranteed. To solve this problem, we employ a nonsmooth analysis technique including differential inclusion and Filippov solution. Through nonsmooth Lyapunov analysis, it is shown that uniformly bounded consensus results are derived and the bound of the consensus error is adjustable by choosing suitable design parameters.

In this paper, we present a distributed economic dispatch strategy for a large-scale power system. At first, we treat each generator and load in the grid as an "agent". By decomposing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner. Due to the large number of the agents, the agents are sorted into several clusters and each cluster has a leader to communicate with the leaders of its neighboring clusters. The agents in the same cluster can conduct local optimization and communicate with its neighboring agents in parallel. After that, the leader agents of each cluster exchange their information simultaneously. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Compared to our previous work in [14], where the leader agent in each cluster conducts the optimization in a sequential way, the proposed scheme in this paper allows them communicate and conduct optimization simultaneously, which greatly improves the algorithm efficiency. A case study implemented on IEEE 30-bus power system are discussed and tested to validate the proposed method.