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Contemporary communications systems use large arrays in order to exploit the spatial domain requiring multiple radio-frequency (RF) chains leading to prohibitive cost and power. Spatial degrees of freedom are also achieved by utilizing the polarization states of an electromagnetic wave. To this end, we propose a polarization-state modulation system based on carrier wave reflections from a reconfigurable intelligent surface (RIS), where a sequence of data bits is mapped to polarization states. We consider a system with an upper millimeter-wave or low-THz RF source so that the channel model is line-of-sight-dominant. The data symbol constellation consists of sixteen quaternion-valued symbols, of which two correspond to linear and the remaining twelve represent elliptical polarizations. Our numerical experiments indicate that the RIS is capable of directing signal power toward the location of the receiver. The bit error rate performance of our rudimentary demodulator, although worse than OFDM-BPSK, is improved using a quaternion neural network.

Many applications in signal processing require the estimation of mean and covariance matrices of multivariate complex-valued data. Often, the data are non-Gaussian and are corrupted by outliers or impulsive noise. To mitigate this, robust estimators are employed. However, existing robust estimation techniques employed in signal processing, such as M-estimators, provide limited robustness in the multivariate case. For this reason, this paper introduces the signal processing community to the highly robust class of multivariate estimators called multivariate S-estimators. The paper extends multivariate S-estimation theory to the complex-valued domain. The theoretical performances of S-estimators are explored and compared with M-estimators through the practical lens of the minimum variance distortionless response (MVDR) beamformer, and the empirical finite-sample performances of the estimators are explored through the practical lens of direction-of-arrival (DOA) estimation using the multiple signal classification (MUSIC) algorithm.

Recent research in ultra-reliable and low latency communications (URLLC) for future wireless systems has spurred interest in short block-length codes. In this context, we analyze arbitrary harmonic bandwidth (BW) expansions for a class of high-dimension constant curvature curve codes for analog error correction of independent continuous-alphabet uniform sources. In particular, we employ the circumradius function from knot theory to prescribe insulating tubes about the centerline of constant curvature curves. We then use tube packing density within a hypersphere to optimize the curve parameters. The resulting constant curvature curve tube (C3T) codes possess the smallest possible latency, i.e., block-length is unity under BW expansion mapping. Further, the codes perform within 5 dB signal-to-distortion ratio of the optimal performance theoretically achievable at a signal-to-noise ratio (SNR) < -5 dB for BW expansion factor n ≤ 10. Furthermore, we propose a neural-network-based method to decode C3T codes. We show that, at low SNR, the neural-network-based C3T decoder outperforms the maximum likelihood and minimum mean-squared error decoders for all n. The best possible digital codes require two to three orders of magnitude higher latency compared to C3T codes, thereby demonstrating the latter’s utility for URLLC.

Recorded Electroencephalogram (EEG) signals are typically affected by interference and artifacts, which can both impact eye reading and computer analysis of the data. Artifacts are induced by physiological (noncerebral) activities of the patient, such as muscular activities of the eyes, or the heart, or the body, while interference may be of external or internal origin. External interference can be induced by electrical machines if the latter are in the same room as the patients, while internal interference can be caused by abnormal breathing, or body movement, or electrode malfunction, or headset movements. Interference may cause severe distortion of EEG signals, resulting in loss of some segments of brain signals, while artifacts are additive signals to brain signals. Therefore, in order to analyze the brain activity signals of a patient, we need to identify and eliminate interference and isolate artifacts. In this paper, we analyze the EEG signals that were recorded using a headset with fourteen channels placed on the heads of comatose patients at the National Institute of Neurology in Tunis, Tunisia. We identify the interference using a robust statistical method known as projection statistics and we separate the brain signals from the artifacts cited above by applying an independent component analysis method. Finally, we show the multifractal behavior of the EEG signals without interference by applying the wavelet leader method and analyze their properties using the singularity spectrum.

A prerequisite to dynamic state estimation of a stochastic nonlinear dynamic model of a power system is its observability analysis. However, due to the model nonlinearity, the traditional methods either suffer from a poor estimation accuracy if a linear approximation is performed or yield an extremely complicated procedure if the Lie-derivative method is applied to a large-scale power system. To address these weaknesses, we propose a new data-driven Koopman-based observability method by revealing the link that exists between the Koopman operator and the Lie-derivative in the Koopman canonical coordinates. This enables the proposed data-driven method not only to be fully derivative-free, which alleviates its implementation complexity but also overcomes the model nonlinearity and inaccuracy of the system. Furthermore, as an important byproduct, the proposed observability analysis scheme provides a valuable guide for the selection of the observables of the Koopman operator, which is a major difficulty for the application of this operator. Finally, we demonstrate the excellent performance of the proposed method on several IEEE standard test systems.

As a model reduction tool, coherency identification has been extensively investigated by power researchers using various model-driven and data-driven approaches. Model-driven approaches typically lose their accuracy due to linear assumptions and parameter uncertainties, while data-driven approaches inevitably suffer from bad data issues. To overcome these weaknesses, we propose a data-driven cumulant tensor-based approach that can identify coherent generators and detect anomalies simultaneously. More specifically, it converts the angular velocities of generators into a fourth-order cumulant tensor that can be decomposed to reflect the coherent generators. Also, using co-kurtosis in the cumulant tensor, anomalies can be detected as well. The simulations reveal its excellent performance.

This paper proposes a novel two-dimensional continuum model that aims to capture the electromechanical wave phenomenon observed in power systems after a disturbance. The model is derived by a homogenization process of the discrete parameters of the conventional, discrete transient analysis model. The implementation of the proposed model is detailed. The paper also demonstrates the accuracy of the proposed model by comparing its results with those obtained from the discrete model. It is observed that the proposed model is able to provide a similar characterization of system electromechanical dynamics as the discrete model from a continuum point of view.

This paper develops a robust dynamic mode decomposition (RDMD) method endowed with statistical and numerical robustness. Statistical robustness ensures estimation efficiency at the Gaussian and non-Gaussian probability distributions, including heavy-tailed distributions. The proposed RDMD is statistically robust because the outliers in the data set are flagged via projection statistics and suppressed using a Schweppe-type Huber generalized maximum-likelihood estimator that minimizes a convex Huber cost function. The latter is solved using the iteratively reweighted least-squares algorithm that is known to exhibit an excellent convergence property and numerical stability than the Newton algorithms. Several numerical simulations using canonical models of dynamical systems demonstrate the excellent performance of the proposed RDMD method. The results reveal that it outperforms several other methods proposed in the literature.

Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters. Recognized as a key metric of community resilience is the social well-being of a community during a disaster, which is made up of mental and physical social health. Other factors influencing community resilience directly or indirectly are emotional health, emergency services, and the availability of critical infrastructures services, such as food, agriculture, water, transportation, electric power, and communications system. It turns out that in computational social science literature dealing with community resilience, the role of these critical infrastructures along with some important social characteristics is not considered. To address these weaknesses, we develop a new multi-agent based stochastic dynamical model, standardized by overview, design concepts, details, and decision (ODD+D) protocol and derived from neuro-science, psychological and social sciences, to measure community resilience in terms of mental and physical well-being. Using this model, we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear, risk perception, information-seeking behaviour, cooperation, flexibility, empathy, and experience, in an artificial society. Furthermore, we simulate this model in two case studies and show that a high level of flexibility, experience, and cooperation enhances community resilience. Implications for both theory and practice are discussed.

This paper summarizes the report prepared by an IEEE PES Task Force. Resilience is a fairly new technical concept for power systems, and it is important to precisely delineate this concept for actual applications. As a critical infrastructure, power systems have to be prepared to survive rare but extreme incidents (natural catastrophes, extreme weather events, physical/cyber-attacks, equipment failure cascades, etc.) to guarantee power supply to the electricity-dependent economy and society. Thus, resilience needs to be integrated into planning and operational assessment to design and operate adequately resilient power systems. Quantification of resilience as a key performance indicator is important, together with costs and reliability. Quantification can analyze existing power systems and identify resilience improvements in future power systems. Given that a 100% resilient system is not economic (or even technically achievable), the degree of resilience should be transparent and comprehensible. Several gaps are identified to indicate further needs for research and development.

We propose a Koopman operator-based surrogate model for propagating parameter uncertainties in power system nonlinear dynamic simulations. First, we augment a priori known state-space model by reformulating parameters deemed uncertain as pseudo-state variables. Then, we apply the Koopman operator theory to the resulting state-space model and obtain a linear dynamical system model. This transformation allows us to analyze the evolution of the system dynamics through its Koopman eigenfunctions, eigenvalues, and modes. Of particular importance for this letter, the obtained linear dynamical system is a surrogate that enables the evaluation of parameter uncertainties by simply perturbing the initial conditions of the Koopman eigenfunctions associated with the pseudo-state variables. Simulations carried out on the New England test system reveal the excellent performance of the proposed method in terms of accuracy and computational efficiency.

Facing stochastic variations of the loads due to an increasing penetration of renewable energy generation, online decision making under uncertainty in modern power systems is capturing power researchers' attention in recent years. To address this issue while achieving a good balance between system security and economic objectives, we propose a surrogate-enhanced scheme under a joint chance-constrained (JCC) optimal power-flow (OPF) framework. Starting from a stochastic-sampling procedure, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo (MC) method, we propose to use a polynomial-chaos-based surrogate that allows us to efficiently evaluate the power-system model at non-Gaussian distributed sampled values with a negligible computing cost. Learning from the MC simulated samples, we further proposed a hybrid adaptive approach to overcome the conservativeness of the JCC-OPF by utilizing correlation of the system states, which is ignored in the traditional Boole's inequality. The simulations conducted on the modified Illinois test system demonstrate the excellent performance of the proposed method.

Dynamic state estimation (DSE) accurately tracks the dynamics of a power system and provides the evolution of the system state in real-time. This paper focuses on the control and protection applications of DSE, comprehensively presenting different facets of control and protection challenges arising in modern power systems. It is demonstrated how these challenges are effectively addressed with DSE-enabled solutions. As precursors to these solutions, reformulation of DSE considering both synchrophasor and sampled value measurements and comprehensive comparisons of DSE and observers have been presented. The usefulness and necessity of DSE based solutions in ensuring system stability, reliable protection and security, and resilience by revamping of control and protection methods are shown through examples, practical applications, and suggestions for further development.

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

Serving as a prerequisite to power system dynamic state estimation, the observability analysis of a power system dynamic model has recently attracted the attention of many power engineers. However, because this model is typically nonlinear and large-scale, the analysis of its observability is a challenge to the traditional derivative-based methods. Indeed, the linear-approximation-based approach may provide unreliable results while the nonlinear-technique-based approach inevitably faces extremely complicated derivations. Furthermore, because power systems are intrinsically stochastic, the traditional deterministic approaches may lead to inaccurate observability analyses. Facing these challenges, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only is free of any linear approximations, but also accounts for the stochasticity of the dynamic model while bringing a low implementation complexity. Furthermore, this approach enables us to quantify the degree of observability of a stochastic model, what conventional deterministic methods cannot do. The excellent performance of the proposed method has been demonstrated by performing extensive simulations using a synchronous generator model with IEEE-DC1A exciter and the TGOV1 turbine governor.

The observability analysis of a time-varying nonlinear dynamic model has recently attracted the attention of power engineers due to its vital role in power system dynamic state estimation. Generally speaking, due to the nonlinearity of the power system dynamic model, the traditional derivative-based observability analysis approaches either rely on the linear approximation to simplify the problem or require a complicated derivation procedure that ignores the uncertainties of the dynamic system model and of the observations represented by stochastic noises. Facing this challenge, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only brings a low complexity, but also enables us to quantify the degree of observability by considering the stochastic nature of the dynamic systems. The excellent performances of the proposed method is demonstrated using simulations of a decentralized dynamic state estimation performed on a power system using a synchronous generator model with IEEE-DC1A exciter and a TGOV1 turbine-governor.

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.

The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantity the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method.

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Loeve expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.

Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in the stochastic economic dispatch (SED) problem for the purpose of forecasting, the conventional Monte-Carlo (MC) method is prohibitively time-consuming for practical applications. To overcome this problem, we propose a novel Gaussian-process-emulator (GPE)-based approach to quantify the uncertainty in SED considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling (LHS) as the computer experimental design, a GPE is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated SED model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

Accurate system state information under various operation conditions is a prerequisite for power grid monitoring and efficient control. To achieve that goal, a new multi-scale state estimation framework is proposed, paving the way for the development of next generation of energy management system (EMS). The developed framework consists of three key components, namely the static state estimation (SSE) module, the dynamic state estimation (DSE) module, the interfaces and switching logics between the two modules. Specifically, the singular spectrum analysis (SSA)-based change point detection approach is developed to monitor the system continuously. If no event is detected by the SSA, the robust SSE using both SCADA and PMU measurements is executed. Otherwise, the event is declared and the results from SSE are used to derive the initial condition for DSE. During the transient process, only PMU-based DSE is executed for system monitoring and it will be terminated when SSA does not detect any change point of the system. After that, the DSE results are forwarded for SSE initialization and bus voltage magnitude and angle estimations. Simulation results carried out on the IEEE 39-bus system demonstrate the effectiveness and benefits of the proposed framework.

This paper combines model-based and data-driven methods to develop a hierarchical, decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level and hence significantly speed up the computational time. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system, and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.

This paper develops a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation. Under Bayesian inference framework, the non-Gaussian posterior distributions of the parameters can be obtained through Markov Chain Monte Carlo (MCMC). However, the latter method suffers from a prohibitive computing time for large-scale systems. To overcome this problem, we develop a decentralized generator model with the PCE-based surrogate, which allows us to efficiently estimate some generator parameter values. Furthermore, the gradient of the surrogate model can be easily obtained from the PCE coefficients. This allows us to use the gradient-based Langevin MCMC in lieu of the traditional Metropolis-Hasting algorithm so that the sample size can be greatly reduced. Simulations carried out on the New England system reveal that the proposed method can achieve a speedup factor of three orders of magnitude as compared to the traditional method without losing the accuracy.

Multimodal imaging techniques have received a great deal of attention, since their inceptions for achieving an enhanced imaging performance. In this paper, a novel joint reconstruction framework for computed tomography (CT) and magnetic resonance imaging (MRI) is implemented and evaluated. The CT and MRI data sets are synchronously acquired and registered from a hybrid CT-MRI platform. Because the image data sets are highly undersampled, the conventional methods (e.g., analytic reconstructions) are unable to generate decent results. To overcome this drawback, we employ the compressed sensing (CS) sparse priors from an application of discrete gradient transform. On the other hand, to utilize multimodal imaging information, the concept of projection distance is introduced to penalize the large divergence between images from different modalities. During the optimization process, CT and MRI images are alternately updated using the latest information from current iteration. The method exploits the structural similarities between the CT and MRI images to achieve better reconstruction quality. The entire framework is accelerated via the parallel processing techniques implemented on a nVidia M5000M Graph Processing Unit. This results in a significant decrease of the computational time (from hours to minutes). The performance of the proposed approach is demonstrated on a pair of undersampled projections CT and MRI body images. For comparison, the CT and MRI images are also reconstructed by an analytic method, and iterative methods with no exploration of structural similarity, known as independent reconstructions. Results show that the proposed joint reconstruction provides a better image quality than both analytic methods and independent reconstruction by revealing the main features of the true images. It is concluded that the structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of joint image recons