In an integrated energy system (IES), fluctuations in coupled district heating networks and renewable energy sources pose risks to the power system’s operation. To effectively mitigate the risk of rare events, it is crucial to identify the key factors that influence their likelihood. Traditional global sensitivity analysis (GSA) assesses the sensitivity of full probability density functions (PDFs) of the system output to uncertain inputs. However, it fails to evaluate the sensitivity of rare event probabilities, which are at the tail of the PDF. It also neglects the uncertainties of the input PDFs, whose hyperparameters can heavily affect the rare event probabilities. To address these issues, we propose a novel framework calledrare-event-based global sensitivity analysis(REGSA) that prioritizes the input PDF parameters that impact the risks of the operating state. We also improve the computational efficiency of this framework by quantifying rare event probabilities through subset simulation and using sparse polynomial chaos expansion (SPCE) in REGSA (hereafter referred to as SPCE-REGSA). The simulations reveal the excellent performance of the proposed method.

Dynamic state estimation (DSE) plays a fundamental role in the monitoring and operation of power systems. Although previous work focuses mainly on traditional synchronous generations, with the increasing penetration of renewables, the estimation of photovoltaic (PV) systems is gaining increasing popularity. However, they primarily address static estimation or adopt an oversimplified dynamic model with a deterministic assumption for solar irradiance. Obviously, this cannot hold in practice, which will inevitably lead to biased estimation results. Facing these problems, this article explores DSE for the first time for a detailed two-stage PV system with the PV array, boost converter, inverter, and filter included. Also, to avoid biased estimation results under solar irradiance variations, we further propose to treat the randomness of solar irradiance as the unknown input of a DSE, which is further analytically merged into the unscented Kalman filter (UKF) framework with an unbiased minimum-variance (UMV) manner. Simulations performed on IEEE standard test systems reveal that even under severe variations of solar irradiation that serve as unknown inputs to the system, the proposed method can produce an unbiased estimate of the dynamic states of the PV, which is also verified in a real-world system. This accurate DSE can serve as a reliable prerequisite for the protection and control of PV-penetrated power systems.

Phasor measurement units (PMUs) are increasingly being deployed in power systems due to their high sampling rates and diverse data sampling types. However, this undoubtedly poses significant challenges to data centers in terms of data storage and transmission. This article proposes an adaptive rank-based tensor ring (TR) method for PMU data compression to address these issues. More specifically, we first extend the orders of the PMU measurement data to achieve high-order tensorization. Subsequently, based on using the alternating least-squares method to decompose the high-order data TR, we introduce a rank-increment strategy to obtain adaptive ranks. Using a TR data structure, the proposed method can transform high-order data with exponentially increasing volumes into a polynomial scale. This allows us to achieve cost-effective PMU data compression. The simulation results using real-world PMU measurement data reveal the excellent performance of our proposed method.

Recent research has proposed media-based modulation (MBM) as a method to reduce the hardware complexity of wireless communications systems and therefore also achieve a reduction of the associated cost. In this work, we propose an MBM system based on a novel asymmetric signal constellation consisting of scaled and shifted Eisenstein integers. The constellation is generated by phase shifts induced by a reconfigurable intelligent antenna, where the magnitudes are modulated by turning on or off certain numbers of reflecting elements. At the receiver, a uniform linear antenna array is used to capture the incident electromagnetic planar wave. Robust estimation techniques, such as the median, the Weiszfeld algorithm, and the$S_{q}$-estimator are employed to recover the constellation points. A novel gain control scheme is proposed together with a phase offset detection method based on circular cross-correlation. Furthermore, complex-valued convolutional neural networks are used as decoders. We consider the performance of our system under impulse noise caused by voltage transients in addition to additive white Gaussian noise and show superior performance vis-Ã -vie a generic 64-QAM modulation scheme and a brute-force arithmetic method based on the four-quadrant arctan function and the median. Furthermore, we compare our system performance with hexagonal QAM-MBM and QAM-MBM.

The concept of Digital Twin has recently emerged, which requires the transmission of a massive amount of sensor data with low latency and high reliability. Analog error correction is an attractive method for low-latency communications; hence, in this paper, we propose the use of complex-valued neural networks and Quaternionic Neural Networks (QNNs) to decode analog codes. Furthermore, we propose mapping our codes to the baseband of the frequency domain to enable easy time and frequency synchronization as well as to mitigate frequency-selective fading using robust estimation theory. This is accomplished by applying inverse Discrete Fourier Transform (DFT) modulation, which achieves a significant reduction in hardware complexity, power, and cost as compared to our previously proposed analog coding scheme. Additionally, we introduce a scaled version of our previous analog codes that enables statistical signal processing, something we have not been able to achieve until now. This achieves significant noise immunity with drastic performance improvements at low Signal-to-Noise Ratios (SNR) and a small loss at high SNR.

A phasor measurement unit (PMU) serves as a superior tool to monitor the dynamics of the power system, but its high cost remains a practical concern that requires the optimal placement of the PMU (OPP). Traditionally, researchers relied on model-based approaches to analyze this problem. However, these methods not only suffer from inevitable parameter uncertainties but can also be computationally expensive for complicated power system dynamic models. Faced with these issues, this article proposes a data-driven OPP approach utilizing an augmented Koopman operator. This operator lifts the original nonlinear state space to a high-dimensional linear Koopman space in a data-driven manner, which fully eliminates the model discrepancy while achieving high computing efficiency. Theoretically, we prove that the observability matrix in the augmented Koopman canonical coordinates preserves the whole dynamic evolution of both the system model and its associated measurement model. Finally, we propose a modified genetic algorithm to solve the established OPP problem, which is enhanced to further accelerate the search speed. The simulation results reveal the excellent performance of our proposed method.

Each year, several disasters occur, resulting in enormous human, infrastructural, and economic losses. To minimize losses and ensure an adequate emergency response, it is vital to prepare the community for greater shock absorption and recovery after an occurrence. This raises the concept of community resilience and also demands appropriate metrics and prediction models for improved preparedness and adaptability. While a community is impacted in three main ways during a disaster, namely social, physical, and cyber there are currently no tools to model their interrelationship. Thus, this article presents a multiagent cyber–physical–social model of community resilience, taking into account the interconnection of power systems, emergency services, social communities, and cyberspace. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics, such as the level of anxiety, risk perception, and cooperation using social sensing, natural language processing, and text mining tools. We examine the suggested paradigm through three different case studies: 1) hurricanes Irma and Harvey; 2) a group of nine agents; and 3) a society comprised of six distinct communities. According to the results, cooperation can positively change individual behavior. Relationships within a community are so crucial that a smaller population with greater empathy may be more resilient. Similar dynamic changes in social characteristics occur when two empathetic communities share resources after a disaster.

This paper proposes a revised version of the robust generalized maximum likelihood (GM)-type unscented KALMAN filter (GM-UKF) for the state estimation of gene regulatory networks (GRNs) in the presence of different types of deviations from assumptions. As known, the parameters and the power of the assumed noises within the GRN model may change abruptly as a result of jump behavior and bursting process in transcription and translation phases. Moreover, there may be outlying samples among genomic measurement data. Some other outliers may also occur in the model dynamics. The outliers may be misinterpreted by the filtering method if not detected and downweighted. To deal with all such deviations, a robust GM-UKF is designed that includes some modifications to address the challenges in calculating the projection statistics in GRNs such as the nonlinear behavior and the natural distance of the states. The proposed filter is compared to four Bayesian filters, i.e., the conventional UKF, the H$_{\infty }$-UKF, the downweighting UKF (DW-UKF), and a modified version of the GM-UKF, the so-called maximum-likelihood UKF(M-UKF). The outcome results demonstrate that the GM-UKF outperforms other methods for all outlier types while the H$_{\infty }$-UKF is appropriate for the changes in noise powers.

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 \leq 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.

In this letter, a two-stage robust unscented unbiased minimum-variance (RU-UMV) estimator is proposed for nonlinear system dynamic state estimation with unknown inputs. In the first stage, by leveraging the statistical linerization and the relationship between unknown input vector and states, we derive a batch-mode regression form. It is shown that the application of weighted least squares for this form yields the same results as the UMV unscented Kalman filter. However, it lacks robustness to outliers. To deal with, robust generalized maximum-likelihood (GM)-estimator together with the projection statistics (PS) is developed, yielding robust state estimates. The latter are further used in the second stage for robust unknown input vector estimation. As a result, both innovation and observation/measurement outliers can be effectively suppressed. Illustrative examples are provided to demonstrate the robustness of the proposed method.

In recent years, linear prediction voice encoders have become very efficient in terms of computing execution time and channel bandwidth usage while providing, in the absence of impulsive noise, natural sounding synthetic speech signals. This good performance has been achieved via the use of a maximum likelihood parameter estimation of an auto-regressive model of order ten that best fits the speech signal under the assumption that the signal and the noise are Gaussian stochastic processes. However, this method breaks down in the presence of impulse noise, which is common in practice, resulting in harsh or non-intelligible audio signals. In this paper, we propose a robust estimator of correlation, the Phase-Phase correlator that is able to cope with impulsive noise. Utilizing this correlator, we develop a Robust Mixed Excitation Linear Prediction encoder that provides improved audio quality for voiced, unvoiced, and transition speech segments. This is achieved by applying a statistical test to robust Mahalanobis distances for identifying the outliers in the corrupted speech signal, which are then replaced with filtered signals. Simulation results reveal that the proposed estimator of correlator outperforms in variance, bias, and breakdown point compared to three other robust approaches based on the arcsin law, the polarity coincidence correlator, and the median-of-ratio estimator without sacrificing the encoder bandwidth efficiency and the compression gain while remaining compatible with real-time applications. Furthermore, in the presence of impulsive noise, the proposed speech encoder speech subjective quality outperforms the state-of-the-art in terms of mean opinion score.

Polynomial-chaos-expansion-based surrogate models have recently been advocated in the literature for power system dynamic parameter estimation. Regarding the estimation of the uncertain generator parameters, a Bayesian inference framework has been proposed based on a polynomial-based reduced-order representation of the synchronous machines using assumed parameter values. Then, the non-Gaussian posterior probability distribution functions (pdfs) of these parameters are recovered through the stochastic sampling approach efficiently. However, facing very large parameter errors, the reliability of the surrogate model decreases, yielding biased estimation results. To overcome this problem, this article develops a hierarchical Bayesian inference framework that processes the measurements provided by phasor measurement units, while making use of multifidelity surrogates together with the importance sampling method. The latter allows us to estimate in an efficient manner the posterior pdfs of the uncertain parameters through the normalized weights of the prior samples. To improve the accuracy of the posterior pdfs, an adaptive procedure is further adopted in the importance sampling for the gradual evolution of its proposal functions. The new proposals assist in fine-tuning the sample space and thereby help to construct surrogates with higher fidelity. Through an iterative process, this approach is able to estimate accurately and efficiently non-Gaussian posterior pdfs of the uncertain generator parameters subject to gross errors.

This letter proposes a new polynomial-chaos-based Kalman filter (PCKF) that is able to track the dynamics of nonlinear dynamical systems subject to strong nonlinearities. Specifically, by resorting to the polynomial chaos theory, the uncertainties of the model and the measurements can be effectively propagated through a set of collocation points. However, this polynomial-chaos-based algorithm suffers from the curse of dimensionality. To overcome this weakness, a dimension reduction strategy is proposed based on variance analysis. This allows us to construct more effective collocations points and to significantly improve the computational efficiency of the PCKF without any loss of estimation accuracy. Simulations carried out on various IEEE systems validate the effectiveness of the proposed method.

This letter proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linearization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like” regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.

In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of measurements from supervisory control and data acquisition (SCADA) system and phasor measurement unit (PMU) are quite different, causing time skewness problem. In this paper, we propose a robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue. In the framework, robust Mahalanbis distances are proposed to detect system abnormalities and assign appropriate weights to each chosen buffered PMU measurements. Those weights are further utilized by the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator to filter out non-Gaussian PMU measurement noise and help suppress outliers. In the meantime, the SHGM estimator is used to handle unknown noise of the received SCADA measurements, yielding another set of state estimates. We show that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution. This nice property allows us to obtain the optimal state estimates by resorting to the data fusion theory for the fusion of the estimation results from two independent SHGM estimators. Extensive simulation results carried out on the IEEE 14, 30 and 118-bus test systems demonstrate the effectiveness and robustness of the proposed method.

We introduce a new geometric construction of cyclic group codes in odd-dimensional spaces formed by intersecting even-dimensional constant curvature curves with hyperplanes of one less dimension. This allows us to recast the cyclic group code as a uniform sampling of a constant curvature curve whereby the design of the constant curvature curve controls code performance. Using a tool from knot theory known as the circumradius function, we derive properties of cyclic group codes from properties of the constant curvature curve passing through every point of the spherical code. By relating the distribution of the squared circumradius function of the connecting curve to the distribution of the pairwise distances of the cyclic group code, we show that the distance spectrum of cyclic group codes achieves optimality in the sense of matching the random spherical code distance distribution bound as the block length grows large.

With increased crowding of the electromagnetic spectrum, interference must be leveraged as an available resource. To address this problem, we develop an array processing technique that provides wireless communications devices with enhanced physical layer secrecy using interference within the same space-time-frequency subspace and the ability to harvest energy by exploiting co-channel interference. This is in contrast to current techniques that attempt to minimize the array response to interference. The proposed directional modulation technique optimizes a set of array steering vectors to enable direction-dependent modulation, thus adding a degree of freedom to the space-time-frequency paradigm. Steering vector selection is formulated as a convex optimization problem for rapid computation given arbitrarily positioned elements. We show that our technique allows us, prior to digitization, to spectrally separate co-channel interference from a desired signal. This technique enables the energy from the interference to be diverted for harvesting during the digitization and decoding of the desired signal. We also show that it is able to transmit to selectively self-interfere in predetermined directions. Finally, we prove that the self-interference can be generated with enough specificity to generate a spoofed signal, providing added security against eavesdroppers.

In this study we introduce a new class of bandwidth-expansion source-channel codes for analog error correction that can work in any even dimensional space and show superior performance in the low SNR region. Our codes are constructed as geodesics on flat tori as previous studies have done, but we use a tube radius construct derived from the global circumradius function. We prove that geodesics on flat tori lead to constant generalized curvature curves and exploit that property to give a single-argument form for the circumradius function. We optimize encoder parameters by minimizing the global radius of curvature subject to harmonic frequency structure which leads to closed twisted tubes with maximal tube radius on the tori. We exploit the isometry of the flat torus with the hyperrectangle to derive simple closed-form decoders based on torus projections that come with 2 dB of the maximum likelihood decoder.

This paper presents a new robust method to estimate the parameters of ARMA models. This method makes use of the autocorrelations estimates based on the ratio of medians together with a robust filter cleaner able to reject a large fraction of outliers, and a Gaussian maximum likelihood estimation which handles missing values. The main advantages of the procedure are its easiness, robustness and fast execution. Its effectiveness is demonstrated on an example of the forecasting of the French daily electricity consumptions.

Catastrophic failures of power systems are phenomena which occur with some regularity throughout the world. It is recognized that these cannot be prevented, although with the use of newer developments in power engineering, in communication systems, and in computer engineering it would be possible to reduce their frequency and their impact on society. Analyses of many blackouts point to some salient features which are common to most such events: power systems under stress because of high load levels or outages of important facilities, some initiating event-usually a fault, often followed by cascading effects due to unwanted operation of some protection systems. In particular, the role of hidden failures (HFs) in protection systems in propagating power system disturbances has become clearer with some of the recent research reported in the literature. This paper explores further the issue of HFs of protection systems and possible countermeasures. Regarding the countermeasures, adapting the protection systems so that they would change their operational logic from OR to a VOTING protocol has been discussed in the literature, and is well within the capability of present technology. Other hardware solutions, such as "Hidden Failure Monitoring and Protection Systems," have also been discussed in the literature. Most of these countermeasures will require intensive use of communication networks. Communication infrastructure will be utilized for real-time data transfer, as well as for slower speed data gathering tasks related to the condition of the power system. In this paper, we concentrate on the communication facilities and their applications for providing countermeasures against catastrophic failures of power systems.

The location, type, and severity of external defects on hardwood logs and stems are the primary indicators of overall log quality and value. External defects provide hints about the internal log characteristics. Defect data would improve the sawyer's ability to process logs such that a higher valued product (lumber) is generated. Using a high-resolution laser log scanner, we scanned and digitally photographed 162 red-oak and yellow-poplar logs. By means of a new robust estimator that performs circle fitting, a residual image is extracted from laser scan data that are corrupted by extreme outliers induced by the scanning equipment and loose bark. The residuals provide information to identify defects with height differentiation from the log surface. Combining simple shape definition rules with the height map allows most severe defects to be detected by determining the contour levels of a residual image. In addition, bark texture changes can be examined such that defects not associated with a height change might be detected.