This article presents a sliding mode control (SMC) strategy to address the finite-time consensus problem of multiagent systems (MASs) under denial-of-service (DoS) attacks. Agents exchange information over network channels that are vulnerable to stochastic DoS attacks, which may disrupt communication and change the network topology. To capture these stochastic variations, a Markov jump model is employed to describe the switching of communication topologies. By introducing a disagreement vector, the consensus problem of the MAS within a finite-time interval is transformed into the stochastic finite-time boundedness (SFTB) problem of the disagreement error dynamic system. A feasible SMC law is developed to drive the disagreement error dynamic system onto a specified sliding surface within a finite time. Furthermore, a partitioning policy is used to ensure the SFTB of the system during both the reaching phase and the sliding phase. A reduced-order approach is used to resolve potential uncontrollability in the system, and sufficient conditions are established to ensure the SFTB of the disagreement error dynamic system under the proposed SMC strategy. Finally, a multiaircraft system example is provided to demonstrate the correctness and effectiveness of the proposed approach.

Service restoration implemented by multiple distributed energy resources (DERs) is a resilience-enhancing paradigm for modern distribution systems. To address the challenges of complex system modeling and the problem of cooperative control over heterogeneous multiple agents, this article proposes a graph reinforcement learning (G-RL) method based on heterogeneous multiagent systems (MASs). The method leverages graph-structured data to enhance the representation of distribution system states and employs graph attention networks (GATs) to deeply explore the power flow features and spatial characteristics of nodes in the restoration process. Additionally, a multihead self-attention (MHSA) is incorporated to strengthen collaboration among heterogeneous agents, enabling them to focus on relevant information from multiple perspectives during training. Finally, a joint simulation test platform is developed using Python and OpenDSS, and case studies on a 123-bus distribution system are conducted. Experimental results demonstrate that the proposed approach achieves efficient and autonomous service restoration by enhancing spatial feature extraction and improving collaborative decision-making among agents.

This article explores the optimal containment control of discrete-time multiagent systems (MASs) with the digraph and unknown dynamics under process disturbances. We first demonstrate, through a model transformation, that the mean square bounded containment of MASs can be achieved by guaranteeing the mean square boundedness of the containment error systems. Hence, we can transform the optimal stochastic containment control problem of MASs into a stochastic optimal control problem for containment error systems. Subsequently, utilizing the Bellman optimality principle and the stochastic Lyapunov equation (SLE), we design a model-based policy iteration (PI) algorithm for the optimal stochastic containment control of MASs. This model-based algorithm, by minimizing the cost function in linear quadratic form, enables MASs to achieve mean square bounded containment with the least possible energy input. To circumvent the dependency on the model information, we introduce an online model-free algorithm for the stochastic optimal control problem. The model-free algorithm is developed based on the Q-learning algorithm. Specifically, it uses a historical MAS trajectory to estimate the kernel matrixHof theQfunction, enabling the resolution of the optimal stochastic containment control problem without model information. To realize the model-free algorithm, the LSTD estimator with bounded bias is employed in the policy evaluation step. We prove the equivalence between the model-free algorithm and the model-based algorithm. Finally, a numerical case is presented to demonstrate the efficacy of the proposed algorithms in achieving the optimal stochastic containment control of MASs.

The large-scale integration of distributed energy resources into active distribution networks may significantly intensify voltage fluctuations and increase network losses. Traditional model-based reactive power optimization approaches depend on existence of accurate system models. On the other hand, conventional reinforcement learning methods largely ignore the spatial characteristics of the active distribution networks during training, allowing agents to have an inadequate perception of the system state. To address these challenges, this paper proposes a multi-agent deep reinforcement learning approach that integrates graph learning with reinforcement learning for the learning of reactive power optimization strategies in active distribution networks. Specifically, the active distribution network is divided into multiple regions, with each region being controlled by an agent. The agents collaborate to achieve the global reactive power optimization goal. The perception capability of the agents is enhanced by adopting graph attention networks during the feature extraction phase. In the training phase, a primal-dual method is employed to manage constraints effectively. During the execution phase, each agent controls the photovoltaic inverters, electric springs, and capacitor banks based on the strategies developed in the training phase. The performance of the proposed approach is validated by a series of experiments on the IEEE-33 system, along with comparisons versus some existing data-driven deep reinforcement learning methods.

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.

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.

Observability and reconstructibility are two fundamental issues in modern control theory, which are important in both state estimation and observer design. The existing results for verifying the observability and reconstructibility of probabilistic logical control networks (PLCNs) have exponential complexities. This article presents a new approach to verify the observability and reconstructibility of PLCNs, which can greatly reduce the computational complexity. Specifically, the problem is tackled in three different steps. Firstly, based on the division of the state pair space, an observability verification system is established. Secondly, the equivalence between the stabilization of the proposed observability verification system and the observability of PLCNs is revealed, and a new criterion is established to solve the observability of PLCNs. Under the framework, the computational complexity is discussed. Thirdly, the relationship between observability and reconstructibility of PLCNs is unveiled, and some new criteria are established to solve two kinds of reconstructibility problems for PLCNs. Finally, an example of a biological network, apoptosis network, is presented to demonstrate the feasibility of the methods proposed in this article.

For voltage sag position tracing (VSPT) through deep learning methods, model performance deteriorates rapidly under changed scenes. Moreover, time and effort are wasted in retraining numerous models for all different scenes. Therefore, a self-adaptive VSPT method which can response to changed scene is urgently needed. In this article, a deep transfer learning for self-adaptive VSPT under changed scenes is proposed. For accurate VSPT under original scene, a deep learning method via temporal iTransformer is presented, which can enhance local feature extraction capability while retaining the iTransformer’s global perspective. For self-adaptive VSPT under changed scenes, a deep transfer learning based on feature-decoupling is further presented. Here, domain invariant features are calculated via feature-decoupling module, and the difference between source domain features and target domain features is adaptively minimized via feature transference. We test the proposed method via simulation and experimental platform, verifying that the proposed deep transfer learning has satisfactory domain adaptability for self-adaptive VSPT under changed scenes.

Recent studies have proposed moving target defense (MTD) to detect false data injection (FDI) attacks in power grids. To hide the activation of MTD from attackers, a hidden MTD (HMTD) has been proposed, which keeps the system power flow after MTD unchanged. It has been proved that HMTD cannot detect all FDI attacks because of its stealthiness requirements. However, the mathematical mechanism of MTD's stealthiness has yet to be revealed. The maximum detection capability of HMTD is also unclear. To address the abovementioned issues, we first analyze the maximum detection capability of HMTD based on graph theory and propose the topological condition to achieve it. Moreover, we study the essential characteristics of HMTD and find that all HMTD schemes are in a space spanned by branch parameters. We further propose a multistage HMTD (MHMTD) method to select multiple HMTD schemes in this space to maximize the detection capability. Experiments show that the MHMTD can maximize the detection capability of HMTD in all test systems with high stealthy probability.

The historical data collected from industrial processes are generally disturbed by ambient noise and outliers. Hence, accurate estimation of process uncertainty is essential in order to correctly determine the status of the process systems. In this study, a robust probabilistic quality-relevant monitoring model with a Laplace distribution is proposed for industrial process monitoring under noisy environment. Because of the heavy tailed characteristic of Laplace distribution, the proposed model is more robust than models with Gaussian distribution. The solution of the proposed probabilistic model is provided through variational Bayesian inference and maximum likelihood estimation after recasting Laplace distribution as Gaussian scale mixtures. Based on the obtained model parameters and estimated latent variables, a quality-relevant monitoring model can be established and four statistics are designed. According to the calculated statistics, the proposed method can effectively detect and differentiate quality-relevant from quality-independent faults. The performance of the proposed method is illustrated using a numerical simulation and a condenser application, which are disturbed by ambient noise and outliers. Experimental results demonstrate that Laplace distribution can better reveal the process uncertainty to effectively alleviate their negative effect. As a result, the proposed method performs better than some commonly used quality-relevant monitoring strategies.

A reliable adaptive-memory-derivative (AMD) event-triggered quantized sliding mode load frequency control (QSMLFC) method is proposed for the multiarea interconnected wind power system under frequency-based deception attacks. An AMD event-trigger scheme is proposed to promote the wind power system operation while saving the network resources, and the reliable AMD event-triggered QSMLFC method aims to reduce the frequency deviations of the interconnected wind power systems. A frequency-based deception attack model is developed for analyzing the security issues in network communications for wind power systems. The hysteresis quantizer is used to lower the communication rate. To validate the correctness of the control method, a sufficient reliability criterion is derived to prove the applicability of the AMD event-triggered QSMLFC. Three numerical examples and an IEEE 39-bus system simulation are presented to demonstrate that the reliable AMD event-triggered QSMLFC method can provide satisfactory stability performance for the wind power system under frequency-based deception attacks.

Due to various reasons, outliers, ambient noise and missing data inevitably exist in the industrial processes, and thus the robustness is important when establishing monitoring models. In this study, a robust dissimilarity analytics model (RDAM) is established with Laplace distribution to detect process anomalies in noisy environment. Because of the heavy-tailed characteristic of Laplace distribution, the proposed RDAM method is more robust to ambient noise and outliers when compared to Gaussian distribution-based models. Besides, the missing data problem is also considered and solved in the model development procedure. Using the variational Bayesian inference, the model parameters and latent variables of the RDAM model can be estimated. After that, a monitoring strategy is designed based on the obtained results with both static and dynamic statistics. By this means, both the static deviation of the current sample and the temporal correlation within the process data can be effectively revealed. A simulated example and a real low-pressure heater process are adopted to illustrate the performance of the proposed RDAM method. Specifically, the proposed RDAM method is robust to the ambient noise and missing values, and it has better detection sensitivity for the process anomalies than the selected comparison methods.

With the advancement of artificial intelligence, deep reinforcement learning is emerging as an effective solution for distribution system service restoration. However, traditional deep reinforcement learning approaches are typically tailored for training agents in specific scenarios, limiting their ability to adapt rapidly to new environments. Furthermore, the spatial characteristics of the distribution network are largely ignored during the training, constraining the state perception capabilities of agents. To address these issues, this paper proposes a meta-based graph reinforcement learning approach that combines graph learning, meta-learning, and reinforcement learning for the learning of service restoration strategies in distribution network. The agent trained by such an approach possesses the feature perception capability of graph learning, allowing it to acquire deeper service restoration strategies from latent graph features. Moreover, the agent also has the fast adaptation ability of meta-learning, enabling it to quickly adapt to new restoration scenarios. Experimental results demonstrate that the proposed approach outperforms existing results of both specialized and generalized strategies.

This paper studies the dynamic state estimation problem for cyber-physical distribution systems (CPDSs) with false data injection attacks (FDIAs) and delayed measurements. In view of the characteristics of multiple measurement types, the equivalent current measurement transformation technique is adopted to make the measurement equation be expressed in the form of linear measurement model. Based on the mixed measurements of phasor measurement units (PMUs) and distribution remote terminal units (DRTUs), a novel model is constructed using Bernoulli distributed random variables to describe the delay phenomena. Further, in order to improve the transmission efficiency of the measurement data, an mechanism is introduced in the network transmission process to minimise the amount of data transmission in the network while ensuring the performance of system state estimation. A measurement model based on the event-triggered mechanism is developed, and an event-triggered square root cubature Kalman filter (ET-SRCKF) algorithm incorporating delayed measurements is designed to implement the state estimation of CPDSs, which can obtain the optimal estimation of the states under delayed measurements. Finally, simulated examples are conducted on the IEEE 33-bus test system, and the effectiveness of the proposed method is illustrated by numerical simulations.

This paper presents a novel non-fragile output sliding mode load frequency control (OSMLFC) strategy designed for multi-area interconnected power systems that incorporate electric vehicles (EVs), particularly in the presence of frequency-triggered denial-of-service (DoS) attacks. We delve into the realm of network communication security concerning load frequency control (LFC) power systems combined with EVs, investigating a real-time frequency-triggered DoS attack by combining real-time frequency dynamics with event-triggering mechanisms. A non-fragile output sliding mode control (SMC) method is proposed, strategically devised to balance the load and frequency aspects of the power systems. Then, a sufficient stability criterion is derived to ensure the non-fragile$H_\infty$stability of the power system integrated with EVs, even when subjected to the perturbations caused by real-time frequency-triggered DoS attacks. The efficacy of our proposed approach and the characteristics of the real-time frequency-triggered DoS attacks are validated through extensive simulations.

The large-scale grouping of the battery system leads to the inconsistency of the battery pack. Aiming at tacking this issue, an inconsistency evaluation method is deployed for the battery pack based on an improved Gaussian mixture model (GMM) and feature fusion approach. Specifically, the proposed adaptive forgetting factor recursive least squares (AFFRLS) algorithm allows the open-circuit voltage and other parameters to be jointly identified without the open circuit voltage-state of charge (OCV-SOC) test. An online capacity estimation approach with the extended Kalman particle filter (EPF) is put forward for capacity estimation. Further, an improved GMM is proposed to visualize battery pack inconsistency, using the K-means++ algorithm to initialize category centers. The standard deviation coefficient approach quantifies the inconsistency. Finally, the real-life vehicle data are performed to validate the effectiveness of the proposed method. The experimental results show that the proposed method can evaluate the battery parameters accurately. With the increase in service time, the inconsistency of the battery pack is gradually deteriorating.

It has been shown that self-triggered control has the ability to deal with cases with constrained resources by properly setting up the rules for updating the system control when necessary. In this article, self-triggered stabilization of the Boolean control networks (BCNs), including the deterministic BCNs, probabilistic BCNs, and Markovian switching BCNs, is first investigated via the semitensor product of matrices and the Lyapunov theory of the Boolean networks. The self-triggered mechanism with the aim to determine when the controller should be updated is provided by the decrease of the corresponding Lyapunov functions between two consecutive samplings. Rigorous theoretical analysis is presented to prove that the designed self-triggered control strategy for BCNs is well defined and can make the controlled BCNs be stabilized at the equilibrium point.

The recent advancements in electroencepha- logram (EEG) signals classification largely center around the domain-specific solutions that hinder the algorithm cross-discipline adaptability. This study introduces a computer-aided broad learning EEG system (CABLES) for the classification of six distinct EEG domains under a unified sequential framework. Specifically, this paper proposes three novel modules namely, complex variational mode de- composition (CVMD), ensemble optimization-based featu- res selection (EOFS), and t-distributed stochastic neighbor embedding-based samples reduction (tSNE-SR) methods respectively for the realization of CABLES. Extensive expe- riments are carried out on seven different datasets from diverse disciplines using different variants of the neural network, extreme learning machine, and machine learning classifiers employing a 10-fold cross-validation strategy. Results compared with existing studies reveal that the highest classification accuracy of 99.1%, 97.8%, 94.3%, 91.5%, 98.9%, 95.3%, and 92% is achieved for the motor imagery dataset A, dataset B, slow cortical potentials, epilepsy, alcoholic, and schizophrenia EEG datasets res- pectively. The overall empirical analysis authenticates that the proposed CABLES framework outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus can be endorsed as an effective automated neural rehabilitation system.

Cooperative sweep coverage of multiagent systems (MASs) has found broad applications in various fields. This article proposes a scheme to address the sweep coverage problem of MASs within uncertain environments. In the proposed formulation, the coverage region is divided into multiple stripes, of which each has the workload completed by MASs in sequence. When the workload on the current stripe is completed, all the agents switch to the next together. The temporal dependence between the switching time computation and the sweep coverage operation is taken into account, and an online learning strategy is designed to handle environmental uncertainties and balance the workload among agents on the same stripe. Thereby, the distributed sweep coverage algorithm is developed to guarantee the complete sweep coverage, which consists of three operations, i.e., communication, workload partition, and sweeping. Theoretical analysis is afterward conducted to estimate the upper bound for the error between the actual and optimal coverage time. Finally, numerical simulations are carried out to substantiate the effectiveness and superiority of the proposed scheme.

In this paper, we consider the target controllability of two-layer multiplex networks, which is an outstanding challenge faced in various real-world applications. We focus on a fundamental issue regarding how to allocate a minimum number of control sources to guarantee the controllability of each given target subset in each layer, where the external control sources are limited to interact with only one layer. It is shown that this issue is essentially a path cover problem, which is to locate a set of directed paths denoted as P and cycles denoted as C to cover the target sets under the constraint that the nodes in the second layer cannot be the starting node of any element in P , and the number of elements in P attains its minimum. In addition, the formulated path cover problem can be further converted into a maximum network flow problem, which can be efficiently solved by an algorithm called maximum flow-based target path-cover (MFTP). We rigorously prove that MFTP provides the minimum number of control sources for guaranteeing the target controllability of two-layer multiplex networks. It is anticipated that this paper would serve wide applications in target control of real-life networks.

The minimum dominating set (MDS) of the network is a node subset of smallest size that every node in the network is either in this subset or is adjacent to one or more nodes of this subset. MDS has found wide applications, ranging from network monitoring, routing, to epidemic control, and text processing. However, the majority of existing studies on MDS problem are confined to single networks. In real world, more and more complex systems consist of a set of elements linked up by different types of connections, which are best modeled as multiplex networks with interacting network layers. Though vastly important, the MDS of the multiplex networks has not yet been formally defined and its application and identification remain open issues. In this article, we present the definition of the MDS of the multiplex network and show some of its possible applications. For solving the MDS problem of the multiplex network, we built a spin-glass model and solve it through the belief-propagation (BP) equations under the replica symmetry mean-field theory. As a consequence, we can predict the relative size of the MDS of the multiplex network theoretically and we can propose a BP-guided decimation algorithm to construct an approximate optimal dominating set in practice. Then the algorithm is improved in both accuracy and efficiency by embedding a novel multiplex network-oriented leaf-removal strategy. The effectiveness of the proposed algorithms is finally verified by comparing with other methods on a number of the multiplex network examples.

High-voltage direct current (HVDC) is increasingly utilized for long-distance electric power transmission, mainly due to its low resistive losses. In this paper, a decentralized control strategy is proposed to address the secondary frequency restoration and real power sharing problem for multi-terminal direct current (MTDC) transmission systems. We establish a sufficient stability condition to guarantee that the designed decentralized leaky integral controller can restore the frequency to its nominal value. Furthermore, the proposed controller can adjust the real power sharing ratio according to different working conditions. An MTDC system consisting of 4 AC systems is built in MATLAB Simulink environment. Numerical simulations are conducted to validate the effectiveness of proposed decentralized control approach.

We consider the quasi-synchronization problem of a continuous time generalized Markovian switching heterogeneous network with time-varying connectivity, using pinned nodes that are event-triggered to reduce the frequency of controller updates and internode communications. We propose a pinning strategy algorithm to determine how many and which nodes should be pinned in the network. Based on the assumption that a network has limited control efficiency, we derive a criterion for stability, which relates the pinning feedback gains, the coupling strength, and the inner coupling matrix. By utilizing the stochastic Lyapunov stability analysis, we obtain sufficient conditions for exponential quasi-synchronization under our stochastic event-triggering mechanism, and a bound for the quasi-synchronization error. Numerical simulations are conducted to verify the effectiveness of the proposed control strategy.

Recent studies have investigated the possibilities of proactively detecting the high-profile false data injection (FDI) attacks on power grid state estimation by using the distributed flexible ac transmission system (D-FACTS) devices, termed as proactive false data detection (PFDD) approach. However, the feasibility and limitations of such an approach have not been systematically studied in the existing literature. In this paper, we explore the feasibility and limitations of adopting the PFDD approach to thwart FDI attacks on power grid state estimation. Specifically, we thoroughly study the feasibility of using PFDD to detect FDI attacks by considering single-bus, uncoordinated multiple-bus, and coordinated multiple-bus FDI attacks, respectively. We prove that PFDD can detect all these three types of FDI attacks targeted on buses or super-buses with degrees larger than 1, if and only if the deployment of D-FACTS devices covers branches at least containing a spanning tree of the grid graph. The minimum efforts required for activating D-FACTS devices to detect each type of FDI attacks are, respectively, evaluated. In addition, we also discuss the limitations of this approach; it is strictly proved that PFDD is not able to detect FDI attacks targeted on buses or super-buses with degrees equalling 1.

In today's communication systems, the most damaging security threats are not originating from the outsiders but from the trusted insiders – both malicious insiders and negligent insiders. Always endowed with high privileges, insiders are significantly prone to conduct acts that can cause catastrophic damages to the whole system either intentionally or unintentionally. Characterised by the full and rapid integration of information and communication technologies, smart grid – arguably the largest national critical engineering infrastructure – is suffering from a multitude of security threats initiated from both outsiders and insiders. Without security guarantee, the promising benefits of achieving an efficient, green, and reliable power grid would not be a success. In this study, the authors investigate the insider threats and summarise the existing threats detection solutions in smart grid communication systems. In addition, a novel hybrid insider threats modelling, analysis, and detection framework, which is based on stochastic Petri net and behaviour rule specifications, is proposed to contain insider threats in smart grid communication systems.

Social sensing relies on a large number of observations reported by different, possibly unreliable, agents to determine if an event has occurred or not. In this paper, we consider the truth discovery problem in social sensing, in which an agent may receive another agent’s observation (known as an information flow), and may change its observation to match the observation it receives. If an agent’s observation is influenced by another agent, we say that the former is a dependent agent. We propose an Iterative Expectation Maximization algorithm for Truth Discovery (IEMTD) in social sensing with dependent agents. Compared with other popular truth discovery approaches, which assume either the agents’ observations are independent, or their dependency is known a priori, IEMTD allows to infer each agent’s reliability, the observations’ dependency and the events’ truth jointly. Simulation results on synthetic data and three real world data sets demonstrate that in almost all our experiments, IEMTD achieves a higher truth discovery accuracy than the existing algorithms when dependencies exist between agents’ observations.

Efficient security patch distribution is of essential importance for updating anti-virus software to ensure effective and timely virus detection and cleanup. In this paper, we propose a mixed strategy of patch distribution to combine the advantages of the traditional centralized patch distribution strategy and decentralized patch distribution strategy. A novel network model that contains a central node and a multiplex network composed of patch dissemination network layer and virus propagation network layer is presented, and a competing spreading dynamical process on top of the network model that simulates the interplay between virus propagation and patch dissemination is developed. Such a new framework helps in effectively analyzing the impacts of patches distribution on virus propagation, and developing more realizable schemes for restraining virus propagation. Furthermore, considering the constraints of the capacity of the central node and the bandwidth of network links, an optimal allocation approach of patches is proposed, which could simultaneously optimize multiple dynamical parameters to effectively restrain the virus propagation with a given budget.

Due to the existence of various anomalies such as non-Gaussian process and measurement noises, gross measurement errors, and sudden changes of system status, the robust forecasting-aided state estimation is pivotal for power system stability. This paper develops a novel unscented Kalman filter (UKF) with the generalized correntropy loss (GCL) (termed as GCL-UKF) to estimate power system state with forecasting aid. The GCL is used to replace the mean square error loss in the original UKF framework. The advantage of such an approach is that it combines the strength of the GCL developed in robust information theoretic learning for addressing the non-Gaussian interference and the strength of the UKF in handling strong model nonlinearities. In addition, we take into account the nontrivial influences of the bad data for the innovation vector. An enhanced GCL-UKF method is established by introducing an exponential function of the innovation vector to adjust a covariance matrix so as to improve the GCL-UKF-based state estimation accuracy under the change of gain matrix caused by bad factors. Numerical simulation results carried out on IEEE 14-bus, 30-bus, and 57-bus test systems validate the efficacy of the proposed methods for state estimation under various types of measurement.

The pervasiveness of information and communications technologies as well as intelligent electronic devices leads to an expanded attack surface in smart grids, making it increasingly challenging to withstand the high-profile false data injection (FDI) attacks. In this paper, we propose a Proactive Approach to Mitigate FDI Attacks (PAMA) in smart grids. With PAMA scheme, the critical information - power grid connections and configurations as well as the original measurement data - used for constructing FDI attacks is well protected from leakage or theft, so that FDI attacks are effectively mitigated. Specifically, we transform the state estimation and FDI detection application into a distributed one equipped with converted information from the critical information provided by the control center. In addition, the original measurement data is also protected by using a secure hybrid Paillier cryptosystem. Our PAMA scheme is proved to be secure and effective in mitigating FDI attacks on smart grids. The computational complexity and the communication overhead are evaluated on the standard IEEE 14-bus test system. Keywords__Smart grids, false data injection (FDI) attack, Paillier cryptosystem, state estimation.

This paper addresses the cooperative control problem of Thyristor-Controlled Series Compensation (TCSC) for eliminating the stress of cyber-physical power system. Specifically, the cyber-physical power system is composed of power transmission network, flexible alternate current transmission systems (FACTS) devices, phasor measurement unit (PMU), and protection and control center. A cooperative control algorithm of TCSC is developed to adjust the branch impedance and redistribute the power flow for the stress relief. To reduce computation burdens, an approximate method is adopted to estimate the Jacobian matrix for producing the TCSC control signals. In addition, a performance index is introduced to quantify the stress level of power system. Theoretical analysis is conducted to guarantee the convergence of performance index when the cooperative control algorithm is implemented in the uncertain environment. Finally, numerical simulations are carried out to validate the cooperative control approach on IEEE 24 Bus Systems with the advantages over the PID control.

Smart grids not only provide "intelligence" to the next generation power systems, but also potentially introduce vital security and privacy issues. Particularly, as a core part of the smart grids, advanced metering infrastructure (AMI) is suffering widespread disputes in terms of security and privacy concerns. This paper proposes a novel hidden Markov model (HMM) based method to detect false data injection attacks in AMI. In this method, a global-state HMM of the whole-house appliances is built and trained by sufficient historical meter data in an offline mode. Then, a new fast Viterbi algorithm is devised to decode the hidden states of the HMM. The decoded states are then verified via the partial sub-meter data in an online mode, by which false data can be detected. The effectiveness and efficiency of our method are verified by a public dataset AMPds with one- year real-time meter data.

In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and energy storages. In such systems, the uncertain renewable energy will impose unprecedented scheduling challenges. To cope with the fluctuate nature of the renewable energy, an uncertainty model based on renewable energies' moment statistics is developed. Specifically, we obtain the mean vector and second-order moment matrix according to predictions and field measurements and then define uncertainty set to confine the renewable energy generation. The uncertainty model allows the renewable energy generation distributions to fluctuate within the uncertainty set. We develop chance constraint approximations and robust optimization approaches based on a Chebyshev inequality framework to firstly transform and then solve the scheduling problem. Numerical results based on real-world data traces evaluate the performance bounds of the proposed scheduling scheme. It is shown that the temporal-correlation information of the renewable energy within a proper time span can effectively reduce the conservativeness of the solution. Moreover, detailed studies on the impacts of different factors on the proposed scheme provide some interesting insights which shall be useful for the policy making for the future microgrids.

This paper investigates the problem of defending against false data injection (FDI) attacks on power system state estimation. Although many research works have been previously reported on addressing the same problem, most of them made a very strong assumption that some meter measurements can be absolutely protected. To address the problem practically, a reasonable approach is to assume whether or not a meter measurement could be compromised by an adversary does depend on the defense budget deployed by the defender on the meter. From this perspective, our contributions focus on designing the least-budget defense strategy to protect power systems against FDI attacks. In addition, we also extend to investigate choosing which meters to be protected and determining how much defense budget to be deployed on each of these meters. We further formulate the meter selection problem as a mixed integer nonlinear programming problem, which can be efficiently tackled by Benders' decomposition. Finally, extensive simulations are conducted on IEEE test power systems to demonstrate the advantages of the proposed approach in terms of computing time and solution quality, especially for large-scale power systems.

The accurately estimated state is of great importance for maintaining a stable running condition of power systems. To maintain the accuracy of the estimated state, bad data detection (BDD) is utilized by power systems to get rid of erroneous measurements due to meter failures or outside attacks. However, false data injection (FDI) attacks, as recently revealed, can circumvent BDD and insert any bias into the value of the estimated state. Continuous works on constructing and/or protecting power systems from such attacks have been done in recent years. This survey comprehensively overviews three major aspects: constructing FDI attacks; impacts of FDI attacks on electricity market; and defending against FDI attacks. Specifically, we first explore the problem of constructing FDI attacks, and further show their associated impacts on electricity market operations, from the adversary's point of view. Then, from the perspective of the system operator, we present countermeasures against FDI attacks. We also outline the future research directions and potential challenges based on the above overview, in the context of FDI attacks, impacts, and defense.

We study the problem of identifying a single infection source in a network under the susceptible-infected-recovered-infected (SIRI) model. We describe the infection model via a state-space model, and utilizing a state propagation approach, we derive an algorithm known as the heterogeneous infection spreading source (HISS) estimator, to infer the infection source. The HISS estimator uses the observations of node states at a particular time, where the elapsed time from the start of the infection is unknown. It is able to incorporate side information (if any) of the observed states of a subset of nodes at different times, and of the prior probability of each infected or recovered node to be the infection source. Simulation results suggest that the HISS estimator outperforms the dynamic message passing and Jordan center estimators over a wide range of infection and reinfection rates.

As the next generation power grid, smart grid is characterized as an informationized system, and demand response is one of its important features to deal with the ever-increasing peak energy usage. However, the supply capacity and required demand make the demand response problem with both spatially and temporally coupled constraints, which, to the best of our knowledge, has not been thoroughly investigated in a distributed manner. The complexity lies in how to guarantee privacy and convergence of distributed algorithms. Aiming at this challenge, in this paper, we first propose a distributed algorithm, which is based on dual decomposition and does not require each user to reveal his/her private information. Then, the convergence analysis is conducted to provide guidance on how to choose the proper step size; through which, we notice that the convergence speed of the subgradient projection method is not fast enough and it is highly dependent on the choice of the step size. Therefore, to increase the convergence rate of the distributed algorithm, we further propose a fast approach based on binary search. Finally, the distributed algorithms are illustrated by numerical simulations and the extensive comparison results validate the better performance of the fast approach.

In this paper, we propose a topology-based Multicast-Capable (MC) nodes placement heuristic - Power Attenuation Constrained Placement (PACP) in amplifier-less power attenuation constrained all- optical metropolitan area network (AL-MAN). We define a selection function in our algorithm to find the 'best' locations in the network for a given number of MC nodes to achieve good performance in overall cost competitiveness (CC) while ensuring that power constraint is always satisfied. Through our simulation studies, we show that PACP is able to construct power constrained trees with cost close to the optimal cost, and also able to improve the CC as high as 6% compared to latest topology-based algorithm. The simulation results conclude that PACP is cost-resilient for amplifier-less optical multicast networks with small number of MC nodes, and with multicast sessions with low source power output.

Limitations are revealed for the existing methods to derive relative gain array (RGA) for a MIMO process containing integrators and/or differentiators. This paper proposes a new method to overcome these limitations. The method can handle processes described by either transfer functions or state-space models. The effectiveness and simplicity of the proposed method are illustrated with examples where the existing methods fail to give valid RGAs.

This paper proposes simple analytic formulas for proportional-integral-derivative (PID) controller tuning for typical process models. The formulas are obtained in a similar way to the simple internal model control (SIMC) tuning rules, while the leading analysis is more delicate. Compared to SIMC counterparts, the new tuning formulas lead to better load disturbance rejection while giving similar setpoint response and peak sensitivity.

Recently, explicit Control Protocol (XCP), Rate Control Protocol (RCP), and Adaptive Proportional-Integral Rate Control Protocol (API-RCP) have been proposed for congestion control, with the main objective of achieving fair and maximum bandwidth utilization. However, studies reveal that both RCP and XCP may suffer continuous oscillations due to misestimating the bottleneck link capacity, and API-RCP may experience oscillations because of its PI adaptivity scheme which involves switching nonlinearity. To avoid these problems, in this paper a way of designing congestion control based on the instantaneous queue sizes in the routers is proposed. The new scheme attains high link utilization and smooth dynamics by clamping the bottleneck queue at a desired size. And it maintains good fairness by allocating the bottleneck bandwidth equally to the competing flows. Simulations are performed to verify the effectiveness of the theoretical design.

In recent years, some emerging and reemerging infectious diseases have grown into global health threats due to high human mobility. It is important to have intervention plans for containing the spread of such infectious diseases. Among various intervention strategies, screening infected people is an efficient way for evaluating the infection scale and controlling the spread of infectious diseases. Considering the cost in manpower and limited screening machines available, we face to challenges for selecting the optimal nodes (sites) in order to obtain better screening and control effects. In this paper, particle swarm optimization technique is used to determine key nodes for controlling infectious disease spread, through evaluating the number of people captured at each key node. The research example is shown on evaluating the screening control over train stations in Singapore. The optimization algorithm and control concept can be easily extended to large-scale infectious disease control in other kinds of key nodes and in other geographical regions. The selection for optimal control set of the multi objective optimization problem is done using particle swarm optimization. Numerical simulation shows the effectiveness of the proposed algorithm.

In this paper, we consider the uplink of a Direct Sequence Code Division Multiple Access (DS-CDMA) system, where the source users cooperate in relaying each other's message to the destination node based on the decode-and-forward relaying protocol. We study the following partner selection strategies: 1. each user helps all other users (All-Cooperate); 2. each user helps all successfully decoded users (Sue-Cooperate); 3. each user helps a user with the maximum observed signal-to-(interference plus noise) ratio (Max-Cooperate). To mitigate the multiple access interference (MAI) due to the usage of non-orthogonal spreading codes, we develop MMSE multiuser detectors at both the cooperative users and the destination node and evaluate the bit-error-rate (BER) performance. Simulation results show that the cooperative relaying significantly outperforms the direct transmission. Among the three partner selection strategies, Max- Cooperate achieves the lowest average BER. It is found that when the number of users increases, the BER performance of direct transmission gets worse, but the BER performance of Max- Cooperate improves significantly. For All-Cooperate and Sue- Cooperate selection strategies, the number of users does not have much impact on the BER performance.

In this paper, we study the impacts of traffic allocations on the performance of optical packet switches (OPS). In particular, two different cases are investigated where traffic loads are distributed over different (i) time slots (time dimension); and (ii) output ports (space dimension), respectively. These two cases are of significant importance as each has different applications and they form the basis of some more complicated schemes. Our main contributions are three fold. Firstly, for the most fundamental OPS configuration, we prove that its packet loss is a convex function of traffic load. For any other node configuration, we prove that its packet loss remains as a convex function of traffic load as long as a simple condition is satisfied. Secondly, for any OPS with its packet loss as a convex function of traffic load, we propose a simple algorithm for efficiently comparing some different traffic allocations and telling which one of them leads to the lowest packet loss. We also show that in either time or space dimension, the best packet-loss performance is achieved when traffic loads are uniformly distributed. Thirdly, for OPS with limited capability of adjusting a given traffic distribution, we propose a Load Balancing (LB) algorithm to minimize the packet loss. These contributions provide some useful guidelines and algorithms for achieving efficient traffic allocations in various OPS networks.

Distributed lightpath provisioning is expected to play a key role in next-generation WDM optical networks. A major challenge in distributed lightpath provisioning is the potentially significant degradation of network blocking performance caused by outdated link-state information, occurring especially under traffic with short average durations of connections. To address this problem, various parallel reservation schemes have been proposed, with the common feature of applying multiple capacity-search and/or reservation operations executed simultaneously. In this paper, we evaluate the performance of these distributed parallel reservation schemes in wavelength-routed networks. Specifically, we develop general yet accurate analytical models to provide insights into the behavior of the different schemes. We also conduct extensive simulation study. Numerical results show that by using simple parallel reservation schemes, in particular through multiple reservations on several different routes, blocking probabilities caused by outdated link-state information can be drastically lowered and network performance can be significantly improved. The tradeoff between control traffic loads and network blocking performance is also evaluated.

An evolutionary algorithm (EA) can be used to tune the control parameters of a construction heuristic to an optimization problem and generate a nearly optimal solution. This approach is in the spirit of indirect encoding EAs. Its performance relies on both the heuristic and the EA. This paper proposes a three-phase parameterized construction heuristic for the shared-path protection problem in wavelength division multiplexing networks with shared-risk link group constraints and applies an EA for optimizing the control parameters of the proposed heuristics. The experimental results show that the proposed approach is effective on all the tested network instances. It was also demonstrated that an EA with guided mutation performs better than a conventional genetic algorithm for tuning the control parameters, which indicates that a combination of global statistical information extracted from the previous search and location information of the best solutions found so far could improve the performance of an algorithm.

Distributed lightpath establishment in wavelength- routed networks requires up-to-date link-state information to achieve blocking performance target. Invalid routing and wavelength assignment decisions caused by inaccurate link-state information may degrade network performance significantly as the lightpaths become more and more dynamic. In this paper, we evaluate the performance of periodic link-state update, where link-state information is exchanged between network nodes at regular intervals. By proposing an accurate analytical model as well as by carrying out extensive simulations, we show how different components of network blocking probability are affected by inaccurate information under different traffic loads, and provide insights into the high sensitivity of blocking performance to link- state update interval under light traffic loads. We demonstrate how the sensitivity could be affected by network connectivity as well.

Being motivated by recent developments in the theory of complex networks, we examine the robustness of communication networks under intentional attack that takes down network nodes in a decreasing order of their nodal degrees. In this paper, we study two different effects that have been largely missed in the existing results: (i) some communication networks, like Internet, are too large for anyone to have global information of their topologies, which makes the accurate intentional attack practically impossible; and (ii) most attacks in communication networks are propagated from one node to its neighborhood node(s), utilizing local network-topology information only. We show that incomplete global information has different impacts to the intentional attack in different circumstances, while local information-based attacks can be actually highly efficient. Such insights would be helpful for the future developments of efficient network attack/protection schemes.

In this paper, we study the benefits of advertising wavelength availability in a distributed lightpath establishment environment by extensive simulations. Various cases with different routing methods, densities of wavelength conversions, and degrees of network connectivity are evaluated. In all these cases, we show that advertising wavelength availability can only improve network blocking performance under light traffic load. In addition, we demonstrate how the performance gain is affected by routing methods, wavelength conversion, and network connectivity.

In this paper, we analyze the blocking probability of distributed lightpath establishment in wavelength-routed WDM networks by studying the two basic methods: destination-initiated reservation (DIR) and source-initiated reservation (SIR). We discuss three basic types of connection blocking: 1) blocking due to insufficient network capacity; 2) blocking due to outdated information; and 3) blocking due to over-reservation. It is shown that the proposed models are highly accurate for both the DIR and the SIR methods, in both the regular and irregular network topologies, under the whole range of traffic loads.

In this paper, we provide a new analytical model for evaluating the blocking performance of dynamic lightpath establishment in multifiber wavelength-routed networks. By adopting the simple link-independent model together with the wavelength correlation assumptions, we manage to achieve a good balance between analytical accuracy and computational complexity. Extensive numerical results show that the proposed model can quickly produce accurate analytical results under different traffic loads and in different networks.

In this work, we propose a new distributed signaling scheme, within the GMPLS framework for establishing lightpaths in wavelength-routed networks with sparse wavelength conversion. Analytical models are developed to evaluate the performance of the proposed scheme. Theoretical and simulation results show that compared to the classic schemed designed primarily for networks with no wavelength conversion, the proposed signaling scheme can achieve much lower blocking probability.

Distributed wavelength provisioning is becoming one of the most important technologies for supporting the next-generation wavelength-routed networks. In this paper we analyze the behavior of wavelength-routed networks with partial wavelength conversion capabilities (i.e., where wavelength conversion is available at only a subset of network nodes) when using distributed wavelength provisioning. Simulation results show that the proposed models are highly accurate for different network topologies under various traffic loads.

A problem of many distributed lightpath provisioning schemes is wavelength contention, which occurs when a connection request attempts to reserve a wavelength channel that is no longer available. This situation results from the lack of updated global link-state information at every node. In networks with highly dynamic traffic loads, wavelength contention may seriously degrade the network performance. To overcome this problem, we propose a new framework for distributed signaling and introduce a class of schemes referred to as intermediate-node initiated reservation. In the new scheme, reservations may be initiated at any set of nodes along the route; in contrast, reservations can only be initiated by the destination node in the classic destination initiated reservation (DIR) scheme. As a result, the possibility of having outdated information due to propagation delay is significantly lowered. Specifically, we consider two schemes within this framework, for networks with no wavelength conversion and for networks with sparse wavelength conversion, respectively. Theoretical and simulation results show that, compared with the classic DIR scheme, the new schemes can significantly improve the network blocking performance. The accuracy of the analytical models is also confirmed by extensive numerical simulations.

In this paper, we analyze the blocking probability of dynamic lightpath establishment in wavelength-routed networks. By using the destination-initiated reservation (DIR) method as a case study, we analyze traffic blocking occurring due to insufficient network capacity as well as traffic blocking caused by outdated information. Simulation results show the proposed models to be highly accurate.

It is cost-effective to install multiple fibers in each link of an all-optical network, because the cost of fibers is relatively low compared with the installation cost. The resulting network can provide a large capacity for good quality of service, future growth, and fault tolerance. If a node has more incoming/outgoing fibers, it requires larger optical switches. Using the current photonic technology, it is difficult to realize large optical switches. Even if they can be realized, they are expensive. To overcome this problem, we design a node configuration for all-optical networks. We exploit the flexibility that, to establish a lightpath across a node, we can select any one of the available channels in the incoming link and any one of the available channels in the outgoing link. As a result, the proposed node configuration requires significantly smaller optical switches while it can result in nearly the same blocking probability as the existing one. We demonstrate that a good network design is to adopt the proposed node configuration and slightly more fibers in each link, so that the network requires small optical switches while it has a small blocking probability.

We develop a model for evaluating the blocking probability of various connection management protocols for wavelength-routed optical networks with dynamic lightpath establishment. The model characterizes both blocking due to insufficient resources and blocking due to multiple interfering connection requests. We then use the analytical model to compare two connection management schemes, one which utilizes source-initiated reservation, and another which utilizes destination-initiated reservation.

We design and optimize the physical topology of all-optical networks. This problem is more challenging than the traditional one for electronic communication networks, because of the wavelength-continuous constraint and it involves routing and wavelength assignment. In this problem, we are given the number of lightpaths required by every node pair and a cost specification, and our objective is to determine a physical topology of minimal cost. We formulate the problem, prove that it is NP-hard, and design an efficient algorithm called two-stage cut saturation algorithm for it. In the first stage, we relax the wavelength-continuous constraint and apply the main idea of the cut saturation method to determine a good initial network. In the second stage, we impose the wavelength-continuous constraint and perform routing and wavelength assignment to establish the specified lightpaths on the initial network. When some lightpaths cannot be established, we apply the main idea of the cut saturation method to optimize the insertion of additional links into the network. Simulation results show the following: (1) the proposed algorithm can efficiently design networks with low costs and high utilization and (2) if wavelength converters are available to support full wavelength conversion, the total cost of the links can be significantly reduced.

For a wavelength-routed network in which connection requests are arriving and departing at high rates, an appropriate control scheme must be implemented to set up light paths for each request in a fast and efficient manner. The control scheme, which includes routing and wavelength assignment algorithms, must also be scalable, and should attempt to minimize the number of blocked connections. In this paper, we consider a distributed control scheme which utilizes a new adaptive routing approach called alternate-link routing. In the proposed approach, routing decisions for a light path are made adaptively on a hop-by-hop basis by individual nodes in a distributed manner. The scheme does not require the maintenance of any global information. A simulation is developed to analyze blocking performance, and it is shown that the proposed approach outperforms fixed routing and, under certain conditions, also outperforms fixed alternate-path routing.

In an all-optical wide area network, some network nodes may handle heavier volumes of traffic. It is desirable to allocate more full-range wavelength converters (FWCs) to these nodes, so that the FWCs can be fully utilized to resolve wavelength conflict. We propose a set of algorithms for allocating FWCs in all-optical networks. We adopt the simulation-based optimization approach, in which we collect utilization statistics of FWCs from computer simulations and then perform optimization to allocate the FWCs. Therefore, our algorithms are widely applicable and they are not restricted to any particular model or assumption. We have conducted extensive computer simulations on regular and irregular networks under both uniform and nonuniform traffic. Compared with the best existing allocation, the results show that our algorithms can significantly reduce: (1) the overall blocking probability (i.e., better mean quality of service) and (2) the maximum of the blocking probabilities experienced at all the source nodes (i.e., better fairness). Equivalently, for a given performance requirement on blocking probability, our algorithms can significantly reduce the number of FWCs required.