The rapid deployment of advanced metering infrastructure facilitates the development of data-driven attack detection methods in smart grids, which typically rely on large amounts of labeled data for training. However, when new types of attacks or faults emerge, security analysts may only intercept limited malicious samples. The scarcity of samples makes it difficult for data-driven methods to learn effective decision boundaries, leading to degraded detection performance. In this work, we bridge the gap by learning class prototype representations from limited samples and learning to match unlabeled samples with corresponding prototypes. Specifically, we propose a meta-learning-based framework termed Learning to Match Prototype (L2MP), which consists of a prototypical network (ProtoNet) that learns prototype representations by aggregating features from labeled samples, and a matching network that assesses the matching degree between unlabeled samples and prototypes for classification. Through episodic training designed to simulate the few-shot setting, L2MP learns to adapt to novel attack and fault types with only a few samples per class. Moreover, we utilize a bilevel optimization strategy to ensure efficient training of both networks. Extensive case studies on smart grid datasets demonstrate that L2MP achieves robust performance under harsh learning conditions and has practical utility in real-world scenarios.

While multi-label learning has shown excellent performance in False Data Injection Attack (FDIA) locational detection, it has also exposed some potential security risks and vulnerabilities. However, unlike the image domain, the vulnerabilities of multi-label learning in the field of power grid have just received attention and urgently need to be explored and addressed. In this paper, to achieve a better understanding for the security risks of deep learning-based multi-label FDIA detectors, we propose two Alternating Direction Method of Multipliers (ADMM) based adversarial attacks, which are applicable to two different scenarios. The proposed two ADMM-based attacks aim to reduce additional attack costs while seeking suitable adversarial perturbations, making the attacks more realistic and feasible. The experimental results verify the effectiveness of the proposed ADMM-based attacks, making noteworthy strides in fostering a profound comprehension of the vulnerabilities in the unique field of deep multi-label learning for power systems.

With the widespread proliferation of electric vehicles (EVs), optimizing the operation of EV charging stations (EVCSs) has become increasingly important. Strategic charging prices can influence both revenue and service efficiency, while differentiated pricing for different EVs can further help mitigate overstay issues. Moreover, under the concept of the peer-to-peer (P2P) sharing economy, energy and carbon allowance trading among EVCSs presents a significant opportunity to reduce both operational costs and carbon emissions. However, limited research has examined such interactions and the specific economic and environmental impacts of joint energy and carbon (E&C) trading in multi-EVCS systems. In this paper, we investigate a joint differentiated pricing and E&C trading problem for multiple EVCSs, aiming to maximize both economic and environmental benefits. Specifically, we first formulate a total revenue-maximization problem that incorporates anxiety-differentiated pricing and P2P E&C trading among multiple interconnected EVCSs. We then propose an operational algorithm to solve the problem based on Nash bargaining and the alternating direction method of multipliers, which can protect privacy and mitigate communication barriers among EVCSs. Simulation results demonstrate that the proposed algorithm can simultaneously improve revenue and achieve low-carbon goals.

The Metaverse, with its promise of immersive experiences and transformative user interactions, represents a new paradigm for IoT development. By integrating IoT with the Metaverse, real-world data can seamlessly enrich virtual environments, offering diverse choices to users. However, the sheer volume of products and user groups in the Metaverse poses challenges in effectively matching users with suitable products. Recommendation systems, particularly Collaborative Filtering (CF), emerge as a solution to this issue, leveraging user preferences and social dynamics. However, traditional CF algorithms face efficiency challenges in the multi-dimensional data landscape of the Metaverse. To address this, a clustering-based CF algorithm is proposed, enhancing recommendation efficiency by leveraging social connections. Additionally, the recommendation system is enhanced with a DSD-Transformer framework, optimizing recommendation accuracy. The experiments indicate that our proposed method may considerably enhance the Metaverse experience when compare to various sophisticated methods and can be utilized to build a range of product recommendation systems.

The Metaverse is a user-generated virtual world, aiming to provide highly personalized experiences for users. A product personalization design platform is a critical direction for the Metaverse’s future development, enhancing user experience by offering personalized services. Blockchain technology ensures the security and privacy of user data, and enables personalized services through smart contracts, offering opportunities for personalization platforms. However, blockchain’s decentralization can lead to excessive product data, resulting in ineffective data management and optimization, subsequently confusing personalized product design styles, which diminishes user experience. To address these issues, this study proposes the product style automatic generation system (PSAGS), centered on an image generation unit. The system outputs images with style information based on the input product text, achieving precise quantization and visualization of product styles, thereby enhancing user engagement and loyalty to the Metaverse. The image generation unit, with a standardization module can standardize the product style, namely, the relationship between product design elements and user emotions, addressing the problem of managing vast style data due to blockchain’s decentralization. The generation module utilizes a diffusion model enhanced with contrastive language-image pretraining (CLIP) to generate style images, deepening the Metaverse experience. Optimizations include dilation convolution in the UNet architecture to enhance image quality and fine-grained CLIP transformations for improved image and text alignment. Results demonstrate the system’s effectiveness in streamlining design processes and improving image quality in personalized product design, with wide applications in the Metaverse.

This article investigates the tracking control of interconnected servo systems, where Denial-of-Service attacks and false-data-injection attacks are considered. In order to ensure the control performance and security, a sliding mode tracking control scheme based on event triggered is designed. First, a mathematical model of the interconnected servo system subjected to hacking is established. Then, an adaptive sliding mode state observer is proposed to cope with the malicious attack. Third, a higher order sliding mode consistent tracking control method is proposed and the convergence of the tracking error is demonstrated. In addition, an event-triggered mechanism is introduced to save communication resources. Finally, simulation results demonstrate the effectiveness of the proposed algorithm.

With the development of intelligent automation technology and advancement of modernization, the degree of interconnection between power systems is increasing. With the main purpose of involving hybrid energy storage systems (HESS) in optimizing system frequency, this work proposes a learning-based tube model predictive control (MPC) for the multi-area interconnected power systems with wind power and HESS. The suggested method has strong adaptability due to the introduction of a new robust constraint handled by a learning mechanism. By identifying the uncertainty set of coupling strength of online data in the learning stage, the optimal MPC problem is calculated in the adaptive stage, which effectively reduces the adverse effects of disturbances and noises in multi-area interconnected power systems. Moreover, an input to state stability criterion is provided to ensure the robust stability of the system with uncertain disturbances and noises. With simulations on a four-area interconnected power system with wind power and HESS, the effectiveness of proposed method is discussed on an improved IEEE 39-bus system.

Existing target segmentation methods are typically considered easy to implement and yield satisfactory results. However, they generally cannot adapt to a new environment without tiring parameter tuning, which leads to poor performance, including issues such as over-segmentation, under-segmentation, missing segmentation, and false positives. To avoid the wrong segmentation in a parameter-tuning-free and user-friendly fashion, we propose a generalized target segmentation (GTS) method based on the image-view representation of point clouds. Specifically, the method avoids devising a Euclidean space-based algorithm that is sensitive to surrounding objects and to the varied point cloud density and intensity in a new environment. The target segment produced by GTS can be used with any target-based extrinsic calibration architecture, based on which this paper further proposes a generalized target-based (in this case, chessboard) extrinsic calibration framework called GTSCalib for a non-repetitive scanning LiDAR and a camera. GTSCalib additionally introduces a novel intensity threshold method based on kernel density estimation (KDE) for 3D corner detection and the SQPnP solver for optimization to achieve more generalized and robust performance. Extensive simulations and experiments demonstrate that GTSCalib has high generalization ability, robustness, and accuracy. The code is released at https://github.com/Natsu-Akatsuki/GTSCalib.Note to Practitioners—Calibration is necessary for many non-repetitive scanning LiDAR-camera systems to enable sensor fusion in the fields of mapping, localization, and perception. Unfortunately, existing target-based (in this case, chessboard) calibration methods are weakly adaptable to the surrounding environment with variable density or intensity of point clouds, resulting in unstable performance, particularly for the target segmentation submodule. To solve this problem, we introduce a new target segmentation approach, GTS, and a more generalized and robust extrinsic calibration framework, GTSCalib. The proposed GTSCalib is very suitable for practitioners looking for a robust and accurate target-based calibration without limits on the target’s pose or its surrounding environment and without the need for time-consuming parameter tuning.

In this paper, the robust control of multi-channel nonlinear delay system is discussed. Firstly, a four-order dynamical model of wind turbine nonlinear system suffers from time-varying delay and match disturbances is established. Then, a channel-dependent fuzzy sliding-mode function is designed and the sliding dynamic is obtained. Meanwhile, the stability analysis of sliding dynamic is proved and the channel-dependent sliding parameters are solved. Furthermore, a fuzzy sliding mode controller is proposed to ensure the sliding surface is reachable. Additionally, an adaptive fuzzy sliding mode controller is presented to deal with the situation that the upper bound of the disturbance is unknown. Finally, a numerical simulation and a semi physical simulation are implemented to demonstrate the effectiveness of the algorithm.Note to Practitioners—This paper was motivated by the issue of the robust control of multi-channel wind turbine nonlinear system. Different from traditional controlled system, the wind turbine nonlinear system often suffers from the delay and disturbances. This paper designs a new fuzzy-sliding-mode strategy to address the above issues. Firstly, we established a four-order nonlinear dynamic of wind turbine system under the time-varying delay and match disturbances. In order to obtain the sliding dynamic, a channel-dependent fuzzy sliding-mode function is presented. For ensuring the sliding surface is reachable, a fuzzy sliding mode controller is proposed. In order to handle the upper bound of the disturbance is unknown, an adaptive fuzzy sliding mode controller is presented. Both simulation and experiment indicate that proposed approaches are feasible and effective.

In this paper, the issues of tracking and synchronization for connected nonlinear servo system is considered. An adaptive neural network (NN) observer-based synthesize strategy is proposed. Firstly, a mathematical model of connected nonlinear isomorphism multi-motor servo system with disturbance is established, where the motors are connected through wired or wireless networks. Then, an adaptive disturbance observer based on sliding-mode is proposed, and the finite time convergence of observation errors has been proven. Thirdly, an adaptive NN tracking strategy is designed, and we have proved the tracking error is bounded and the full-state asymmetric constraints are satisfied. Furthermore, a synchronous control technique on sliding-mode is presented, and both the boundedness of synchronous error and reachability of sliding surface are verified. Finally, a numerical simulation and a semi-physical simulation are carried out to illustrate the validity of proposed methods.

A memory output sliding mode load frequency control (MOSMLFC) strategy is proposed for multi-area interconnected power systems under historical-frequency-triggered denial-of-service (DoS) attacks. Due to the use of the open network, the multi-area power system is prone to cyber-attacks. Different types of attack models have been built to describe the actual attack behavior, so that effective strategies can be quickly formulated in the event of an attack. Therefore, a historical-frequency-triggered DoS attacks model is presented from the perspective of attackers, with the aim of destroying the stable state of the multi-area power system. It is assumed that attackers determine the timing of DoS attacks by monitoring the operational status of multi-area power systems and designing the triggering condition with historical frequency. A MOSMLFC strategy is investigated to ensure the security performance of multi-area power systems under historical-frequency-triggered DoS attacks, which applies the memory output information of the power system to realize the controller design. The security condition of multi-area power systems under historical-frequency-triggered DoS attacks is obtained by Lyapunov’s theorem and linear matrix inequality (LMI). Numerical examples are tested over the IEEE 10-generator 39-bus system and the results prove the usefulness and superiority of the proposed method. Note to Practitioners—Load frequency control is widely applied in multi-area power systems to achieve a balance between the load demand and generation. Frequent cyber-attacks are a threat to the normal operation of the power system. It is therefore necessary to develop appropriate strategies to defend against cyber-attacks. So far, there have been many different forms of cyber-attacks. This has prompted defenders to build different types of attack models to describe the actual attack behavior in order to preemptively formulate appropriate defensive strategies. Smart attacker may notice that certain characteristics of the target system are important, such as the power system frequency. This motivates us to propose a historical frequency-triggered DoS attack model that contributes to a deep understanding of the impact of cyber-attacks on the power system. We propose a unique sliding mode control approach to ensure the stable performance of power system state and output simultaneously.

This study focuses on anti-quasisynchronization for discrete-time asynchronous leader-follower Markovian neural networks (MNNs) with mismatched parameters. To overcome the energy constraint, the intermittent control transmission strategy is introduced. Meanwhile, to address the challenge of unknown Markovian models in the leader-follower MNNs, a hidden Markov model (HMM) is utilized to infer unknown modes from observable information. Then, an intermittent nonfragile controller based on HMM is designed for the follower MNNs. Furthermore, the exponential iteration method is employed to establish sufficient conditions for ensuring anti-quasisynchronization for leader-follower MNNs, and an optimal boundary of anti-quasisynchronization is obtained. Ultimately, the effectiveness of the proposed HMM-based intermittent controller is demonstrated via a numerical simulation.

Despite the success of data-driven methods in detecting false data injection (FDI) attacks, the remarkable progress is inseparable from massive labeled and class-balanced measurements. However, the collected measurement datasets in smart grids typically exhibit skewed class distributions and are partially labeled due to the expensive labeling costs. Learning from such non-ideal datasets undoubtedly results in the degenerated detection performance of the data-driven methods. To cope with this issue, we propose an optimal transport (OT)-based framework named DeSSW to promote the utilization of plentiful unlabeled measurements through the self-training technique, which improves the ability to identify FDI attacks by producing distinguishable representations for normal and attacked measurements in the feature space. Specifically, DeSSW consists of a novel re-weighting algorithm and a debiased self-training strategy. The re-weighting algorithm ensures high-confidence unlabeled measurements dominate the self-training procedure, and the debiased self-training strategy mitigates bias accumulation in the iterative self-training procedure. Extensive experiments demonstrate that DeSSW achieves superior detection performance when facing the combinatorial challenge of partially labeled and class-imbalanced measurements, even if the measurements are noisy.

This paper investigates a non-fragile robust security control strategy for remote motors, based on a dynamic threshold cryptographic detector. This strategy aims to protect system performance against stealthy false data injection (FDI) attacks and to effectively minimize the impact of controller jitter. First, a stealthy FDI attack is designed to bypass the conventional$\chi ^{2}$detector and degrade system performance. The stealthiness and destructiveness of the attack are demonstrated. Next, to counter the stealthy FDI attack, a dynamic threshold cryptographic detector is proposed. This detector addresses the stealthiness of the attack and enhances robustness by incorporating a time-varying nonlinear function and a dynamic threshold detection strategy. Furthermore, a non-fragile robust security control strategy is introduced to prevent these attacks and mitigate the problem of controller perturbations. The stability of this strategy is proven using Lyapunov theory. Finally, the effectiveness of the proposed security control strategy is validated through numerical and semi-physical simulations.

The initial alignment is a critical stage for a strapdown inertial navigation system (SINS) and global positioning system (GPS) integrated navigation system. Currently, two major factors degrade the performance of SINS/GPS in-motion initial alignment, i.e., outliers in GPS measurements and cumulative low-accuracy inertial measurement unit (IMU) bias errors. This article considers both factors and proposes GISEIA-EMM: a high-accuracy GPS-inertial state estimator for in-motion alignment based on extended magnitude matching (EMM) method. First, we use the full integral method and non-interpolation procedure to construct the vector observation, which reduces the number of outliers and improves the accuracy of outlier detection. Second, we use an error-state extended Kalman filter (ESEKF), based on an augmented state-space model where the reference vector is regarded as a state, to suppress cumulative IMU bias errors, which improves the alignment accuracy. Third, we propose an EMM method, with the non-drifted expected normalized magnitude error, to detect and eliminate outliers in GPS measurements, which makes the alignment process stable. Simulation and field test results demonstrate that GISEIA-EMM can effectively address the negative impact of the two factors.

This article investigates the secure control of a class of servo DC motors in the presence of input–output disturbances and DoS attacks. A multichannel observer-based switchingH∞ control strategy is proposed and a dynamic event triggering mechanism (DETM) is designed to save network resources. First, a mathematical model of servo DC motor containing input–output disturbances is developed and discretized to make it more suitable for computer control. Then, a state observer and a multichannel transmission strategy based on Markov theory are designed in order to obtain the accurate knowledge of disturbed system and transmit it to the remote controller under DoS attack. Third, observer-based state feedback switchingH∞ control strategy is proposed and the stability is demonstrated. Furthermore, the DETM is presented to reduce the occupation of network resources by introducing dynamic trigger variable. Finally, the performance of the characterized control strategy is verified by a numerical simulation and a semi-physical simulation.

One of the major sources of conservativeness in min-max model predictive control (MPC) is the handling of constraints, where the ellipsoidal robust invariant set is utilized for the proposal of sufficient and conservative conditions for the satisfaction of the constraints. In this article, in order to reduce the conservativeness due to the constraint handling, we add additional relaxation variables to the physical constraints and propose a two-step approach through relaxing the constraints by the amount which is determined by the calculating of the maximal admissible set (MAS). The constraints are relaxed in an iterative manner to avoid the constraints violation, and the constraint relaxation variables are degrees of freedom for relaxing the constraints and improving the control performance. Moreover, we show that under certain circumstance, the physical constraints can be removed without the constraint violation. The proposed approach is shown to be recursively feasibility and its effectiveness is verified through an air conditioning control in a building energy system. Note to Practitioners—Buildings are account for large percentage of worldwide energy consumptions. One of the most applicable method for optimization of building energy system subject to multiple constraints is the model predictive control (MPC). However, the industrial MPC is usually not recursively feasible, which implies that the optimization problem can become infeasible and the software will be terminated at some time. In order to apply the MPC synthesis approach (MPC with recursive feasibility guarantee), we have to overcome the conservativeness problem due to the handling of constraints. We propose a useful approach in this work by introducing relaxation variables which act as degrees of freedom for improving the control performance, while the physical constraints are still satisfied. The proposed approach is verified through an example of a 24m2 office room located in Cyber-Physical Energy System (CPES) lab in Western China Science and Technology Innovation Harbour in Xianyang, China. The numerical results show the performance improve of the proposed approach.

Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids.

This article considers the dynamic output feedback model predictive control (DOFMPC) for the constrained Takagi-Sugeno (T-S) model with bounded disturbance. Unlike the existing approach where the robust positively invariant set is characterized by a single ellipsoid, we characterize it by the intersection of multiple ellipsoids, each corresponds to a vertex sub-model of the T-S model realization. The previous single ellipsoid is then an inner approximation of the intersection of multiple ellipsoids in this article. Therefore, the performance can be improved. We also generalize the multi-ellipsoid approach to the previous multi-step approach and formulate the so-called multi-step multi-ellipsoid approach in this article, which can further enlarge the feasibility region and enhance the performance. The recursive feasibility and the convergence of the approach are guaranteed. The proposed approaches are compared through a numerical problem to show their effectiveness.

Load frequency control (LFC) is widely employed in power systems to stabilize frequency fluctuation and guarantee power quality. However, most existing LFC methods rely on accurate power system modeling and usually ignore the nonlinear characteristics of the system, limiting controllers' performance. To solve these problems, this article proposes a model-free LFC method for nonlinear power systems based on deep deterministic policy gradient framework. The proposed method establishes an emulator network to emulate power system dynamics. After defining the action-value function, the emulator network is applied for control actions evaluation instead of the critic network. Then, the actor network controller is effectively optimized by estimating the policy gradient based on zeroth-order optimization and backpropagation algorithm. Simulation results and corresponding comparisons demonstrate the designed controller can generate appropriate control actions and has strong adaptability for nonlinear power systems.

Information and communication technology tremendously facilitates the operation efficiency and economy of modern power systems in recent years. However, risks such as bandwidth constraints and malicious attacks threaten the secure load frequency control (LFC) of power systems. To mitigate such risks, this paper proposes distributed observer-based event-triggered LFC schemes for multi-area power systems under cyber attacks. Considering the practical situation that only local system output information may be available, distributed observer-based LFC schemes are designed. Meanwhile, to reduce the communication burden, an event-triggered mechanism is adopted to design control laws, where both static and dynamic event-triggered approaches are taken and the dynamic one is proved to be more economical in terms of control cost. Verifiable sufficient conditions are established to guarantee the stability of the closed-loop system in the presence of cyber attacks and the controller gains are explicitly derived. Finally, validation studies on a three-area interconnected power system are carried out to demonstrate the proposed control schemes. Note to Practitioners—Load frequency is a crucial index for evaluating the quality of electric energy and thus LFC has brought considerable attention in the area of power systems control. Although many achievements have been made on the LFC of multi-area power systems, the risks such as bandwidth constraints and malicious attacks affect the normal operation of LFC due to the interconnection between different power systems. To deal with these risks, this paper focuses on designing event-triggered LFC to guarantee the stability of the frequency deviation while reducing the communication burden and mitigating cyber attacks. More importantly, the proposed event-triggered LFC is designed based on an observer and can be implemented in a distributed manner, which is relatively practical in real applications. The results presented in this paper aim to provide a helpful reference for stable and secure LFC design of multi-area power systems, such that the corresponding application research can be promoted.

Load frequency control (LFC) is crucial for the economic operation and safety of power systems. Therefore this paper addresses the LFC problem for uncertain multi-area power systems with actuator failures. Specifically, actuator failures, uncertainties and communication bandwidth constraints appearing in multi-area power systems are taken into account simultaneously, and novel reliable event-triggered LFC schemes are proposed to cope with these troubles. The proposed schemes can ensure the asymptotical stability of the closed-loop system when only matched uncertainty exists. For the case of coexisting matched and mismatched uncertainties, the state trajectories of the closed-loop system can be controlled within a bounded set, where the size of the bounded set is only related to the mismatched uncertainty. To illustrate the theoretical results, a numerical example of three-area interconnected power system is presented. Note to Practitioners—Load frequency directly affects the quality of electric energy and is one of the main observation states of power systems, hence LFC has been widely investigated in the literature. For multi-area power systems, the system model to be controlled may be subjected to multiple unfavorable factors in practical situations, such as limited bandwidths, model uncertainties and actuator failures. To cope with these unfavorable factors, this paper is devoted to developing a unified control framework to guarantee the stability of the frequency deviation based on the event-triggered mechanism. Considering both matched and unmatched system uncertainties may exist as well as the bound of system uncertainties can be unknown, event-triggered control schemes including static event-triggered LFC and adaptive event-triggered LFC are accordingly designed to deal with aforementioned situations such that the closed-loop system is asymptotically or boundedly stable. The research outcome of this paper provides simple but effective LFC approaches that can be used to maintain the reliable and stable operation of multi-area power systems.

In this article, a novel approach to hierarchical implementation of output feedback robust model predictive control is proposed for the linear polytopic uncertain model. One optimization problem for minimizing the performance index is followed with the other assessing estimation error set (EES). The two problems are posed in a lexicographic order. Since in the latter problem, the controller parametric matrices are retaken as the degrees of freedom for the optimization, a much less conservative EES is calculated. Therefore, by applying the new approach, the control performance can be greatly improved as compared with the earlier schemes without lexicographic optimization. The proposed approach is proven to be recursively feasible, and the closed-loop stability is specified by the notion of quadratic boundedness. The result is verified through two numerical examples.

In order to ease the online computational burden of fuzzy model-predictive control for the Takagi–Sugeno model with bounded disturbance, a lookup table containing the possible mappings from the state to the input is usually constructed offline so that the online computational burden is reduced to searching in this lookup table. However, with the increase in the problem size, the computational burden of the online search in the lookup table can be large enough to influence the real-time implementation. In this article, we propose a novel offline approach to solve this problem, where the control law is online searched in a receding horizon cache, which is only a small portion of the lookup table. The cache is refreshed in a one-step-ahead fashion to guarantee that the proper one-step-ahead state-to-input mapping can be found in the cache. The recursive feasibility and stability hold. The effectiveness and the efficiency of the proposed approach are verified through two examples.

The operation of smart grids heavily relies on secure and accurate meter measurements provided by phasor measurement units (PMUs). Therefore, the optimal PMU placement (OPP) aiming to achieve the complete system observability of smart grids with as few PMUs as possible has been extensively investigated. Although many existing studies have focused on the OPP, few of them are concerned with the placement order of PMUs. To protect as many buses as possible in smart grids when installing PMUs in stages owing to high cost, this paper proposes the attack-resilient OPP strategy which places PMUs in order by using reinforcement learning guided tree search, where the sequential decision making of reinforcement learning is utilized to explore placement orders. The least-effort attack model is carried out to screen vulnerable buses such that the buses adjacent to these buses can be placed PMUs in advance to reduce the state space and action space of the large-scale smart grid environment. Based on that, the reinforcement learning guided tree search approach is used to explore the key buses which need placing PMUs, where the repeated exploration of the agent is avoided by tree search. Then, a reasonable placement order of PMUs is obtained according to the action sequence the proposed method provides. Finally, the effectiveness of the proposed method is verified on various IEEE standard test systems and the comparison results with existing methods are provided.

In interconnected industrial control networks like smart grids, topology attacks on physical grids can lead to severe cascading failures and large-scale blackouts. Effective defense on vulnerable devices can significantly reduce the risk of cascading failures and improve overall system robustness. In this paper, we investigate the vulnerability analysis problem from a graph theoretical classification perspective. By calculating a node vulnerability vector composed of features based on complex network theory, node embedding, extended betweenness and power flow distribution, we propose a node vulnerability analysis method based on XGBoost classifier. A cascading failure simulation model based on DC power flow is used to simulate the smart grid behaviours under topology attacks and create the dataset for the XGBoost classifier. The effectiveness of the proposed XGBoost-based method with newly-introduced features is demonstrated by case studies.

Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy efficient and green buildings. However, it is a nontrivial task due to the following challenges. First, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Second, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Third, there are many spatially and temporally coupled operational constraints. Fourth, building energy optimization problems can not be solved in real time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this article provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.

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 investigates the dissipative filtering problem for a class of discrete-time switched fuzzy systems with missing measurements. The fuzzy plant under consideration incorporates characteristics of Takagi-Sugeno fuzzy systems and switched systems simultaneously. The occurrence of missing measurements is described by a stochastic variable that satisfies the Bernoulli binary distribution, which characterizes the effect of data loss in information transmission between the plant and the filter. Utilizing the Lyapunov function technique, sufficient conditions are developed to ensure that the resultant filtering error system is exponentially stable and strictly dissipative. Two simulation examples are presented to illustrate the validity of the proposed method.

This paper is focused on the static output feedback (SOF) control problem for a class of switched nonlinear systems with actuator faults. By means of the Takagi-Sugeno fuzzy model, the switched nonlinear plant is described by a family of switched fuzzy systems. Considering transmission failures may occur between controller and actuator, a reliable SOF controller against actuator faults is designed. Sufficient conditions are developed to guarantee the existence of the reliable SOF controller. Furthermore, an iterative algorithm is designed to determine the controller gains, which avoids the conservatism brought by the traditional singular value decomposition method. To validate the effectiveness of the proposed approach, a numerical example is exploited and simulation results are also presented.

This paper addresses the problem of quantized feedback control of nonlinear Markov jump systems (MJSs). The nonlinear plant is represented by a class of fuzzy MJSs with time-varying delay based on a Takagi-Sugeno fuzzy model. The quantized signal is utilized for control purpose and the sector bound approach is exploited to deal with quantization errors. By constructing a Lyapunov function which depends both on mode information and fuzzy basis functions, the reciprocally convex approach is used to derive the criterion which is able to ensure the stochastic stability with a predefined l2- l∞performance of the resulting closed-loop system. The design of the quantized feedback controller is then converted to a convex optimization problem, which can be handled through the linear matrix inequality technique. Finally, a simulation example is presented to verify the effectiveness and practicability of the proposed new design techniques.