- Complex Systems, Nonlinear dynamics, Electric Power Systems, Petri Nets, Distributed Algorithms, Comparison, and 8 moreAnalog/mixed signal integrated circuit design, Estimation and Filtering Theory, Blackouts, Adaptive Control, Intelligent Systems, Agile Methods (Software Engineering), Probability and Mathematical Statistics, and Stochastic Modelingedit
- Dr. Mili is a Power Professor and Program Director of the Electrical and Computer Engineering Department at Virginia ... moreDr. Mili is a Power Professor and Program Director of the Electrical and Computer Engineering Department at Virginia Polytechnic and State University’s Northern Virginia Center. He has five years of industrial experience with the Tunisian electric utility, STEG. At STEG, he worked in the planning department from 1976 till 1979 and then at the Test and Meter Laboratory from 1979 till 1981. He participated in the commissioning of a steam power plant in 1980 in Tunisia. He was a visiting professor at the Swiss Federal Institute of Technology in Lausanne, Switzerland, the Grenoble Institute of Technology and the École Supérieure d’Électricité in in France, the École Polytechnique de Tunisie, in Tunisia, and did consulting work for the French Power Transmission company, RTE. His research has focused on power system planning for enhanced resiliency and sustainability, risk management of complex systems to catastrophic failures, robust estimation and control, non-linear dynamics, and bifurcation theory.edit
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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... more
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.
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The least median of squares (LMS) estimator minimizes the v th ordered squared residual. The authors derived a general expression of the optimal v for which the breakdown point of the LMS attains the highest possible fraction of outliers... more
The least median of squares (LMS) estimator minimizes the v th ordered squared residual. The authors derived a general expression of the optimal v for which the breakdown point of the LMS attains the highest possible fraction of outliers that any regression equivariant estimator can handle. This fraction is equal to half of the minimum surplus divided by the number
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AbstractLarge-scale blackouts typically result from cascading failure in power systems operation. Their mitigation in power system planning calls for the development of methods and algorithms that assess the risk of cascading failures... more
AbstractLarge-scale blackouts typically result from cascading failure in power systems operation. Their mitigation in power system planning calls for the development of methods and algorithms that assess the risk of cascading failures due to relay overtripping, ...
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This paper describes a fast and rabust method for identifying the leverage points of a linearized power system state estimation model. These are measurements whose projections on the space spanned by the row vectors of the weighted... more
This paper describes a fast and rabust method for identifying the leverage points of a linearized power system state estimation model. These are measurements whose projections on the space spanned by the row vectors of the weighted Jacobian matrix, the so-called factor space, do not follow the pattern of the bulk of the point cloud. In other words, their projections are outliers in the factor space. The proposed method is implemented through a new version of the projection algorithm that accounts for the sparsity of the Jacobian matrix. It assigns to each data point a projection statistic defined as the maximum of the standardized projections of the point cloud on some directions passing through the origin. Based on these projection statistics, a robustly weighted Schweppe-type GM-estimator is defined, which can be computed by a reweighted least squares algorithm. The computational efficiency and the robustness of the method are demonstrated on the IEEE-14 bus and the 118-bus systems.
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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... more
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.
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Abstract. Tomographic image reconstruction requires precise geometric measurements and calibration for the scanning system to yield optimal images. The isocenter offset is a very important geometric parameter that directly governs the... more
Abstract. Tomographic image reconstruction requires precise geometric measurements and calibration for the scanning system to yield optimal images. The isocenter offset is a very important geometric parameter that directly governs the spatial resolution of reconstructed images. Due to system imperfections such as mechanical misalignment, an accurate isocenter offset is difficult to achieve. Common calibration procedures used during isocenter offset tuning, such as pin scan, are not able to reach precision of subpixel level and are also inevitably hampered by system imperfections. We propose a purely data-driven method based on Fourier shift theorem to indirectly, yet precisely, estimate the isocenter offset at the subpixel level. The solution is obtained by applying a generalized M-estimator, a robust regression algorithm, to an arbitrary sinogram of axial scanning geometry. Numerical experiments are conducted on both simulated phantom data and actual data using a tungsten wire. Simulation results reveal that the proposed method achieves great accuracy on estimating and tuning the isocenter offset, which, in turn, significantly improves the quality of final images, particularly in spatial resolution.
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ABSTRACT Since its recent inception, simultaneous image reconstruction for multimodality fusion has received a great deal of attention due to its superior imaging performance. On the other hand, the compressed sensing (CS)-based image... more
ABSTRACT Since its recent inception, simultaneous image reconstruction for multimodality fusion has received a great deal of attention due to its superior imaging performance. On the other hand, the compressed sensing (CS)-based image reconstruction methods have undergone a rapid development because of their ability to significantly reduce the amount of raw data. In this work, we combine computed tomography (CT) and magnetic resonance imaging (MRI) into a single CS-based reconstruction framework. From a theoretical viewpoint, the CS-based reconstruction methods require prior sparsity knowledge to perform reconstruction. In addition to the conventional data fidelity term, the multimodality imaging information is utilized to improve the reconstruction quality. Prior information in this context is that most of the medical images can be approximated as piecewise constant model, and the discrete gradient transform (DGT), whose norm is the total variation (TV), can serve as a sparse representation. More importantly, the multimodality images from the same object must share structural similarity, which can be captured by DGT. The prior information on similar distributions from the sparse DGTs is employed to improve the CT and MRI image quality synergistically for a CT-MRI scanner platform. Numerical simulation with undersampled CT and MRI datasets is conducted to demonstrate the merits of the proposed hybrid image reconstruction approach. Our preliminary results confirm that the proposed method outperforms the conventional CT and MRI reconstructions when they are applied separately.
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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... more
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.
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This dissertation presents an automated detection algorithm that identifies severe external defects on the surfaces of barked hardwood logs and stems. The defects detected are at least 0.5 inch in height and at least 3 inches in diameter,... more
This dissertation presents an automated detection algorithm that identifies severe external defects on the surfaces of barked hardwood logs and stems. The defects detected are at least 0.5 inch in height and at least 3 inches in diameter, which are severe, medium to large in size, and have external surface rises. Hundreds of real log defect samples were measured, photographed, and categorized to summarize the main defect features and to build a defect knowledge base. Three-dimensional laser-scanned range data capture the external log shapes and portray bark pattern, defective knobs, and depressions. The log data are extremely noisy, have missing data, and include severe outliers induced by loose bark that dangles from the log trunk. Because the circle model is nonlinear and presents both additive and non-additive errors, a new robust generalized M-estimator has been developed that is different from the ones proposed in the statistical literature for linear regression. Circle fitting is performed by standardizing the residuals via scale estimates calculated by means of projection statistics and incorporated in the Huber objective function to bound the influence of the outliers in the estimates. The projection statistics are based on 2-D radial-vector coordinates instead of the row vectors of the Jacobian matrix as proposed in the statistical literature dealing with linear regression. This approach proves effective in that it makes the GM-estimator to be influence bounded and thereby, robust against outliers. Severe defects are identified through the analysis of 3-D log data using decision rules obtained from analyzing the knowledge base. Contour curves are generated from radial distances, which are determined by robust 2-D circle fitting to the log-data cross sections. The algorithm detected 63 from a total of 68 severe defects. There were 10 non-defective regions falsely identified as defects. When these were calculated as areas, the algorithm locates 97.6% of the defect area, and falsely identifies 1.5% of the total clear area as defective.
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This report of TF on dynamic state and parameter estimation aims to 1) clearly review its motivations and definitions, demonstrate its values for enhanced power system modeling, monitoring, operation, control and protection as well as... more
This report of TF on dynamic state and parameter estimation aims to 1) clearly review its motivations and definitions, demonstrate its values for enhanced power system modeling, monitoring, operation, control and protection as well as power engineering education; 2) provide recommendations to vendors, national labs, utilities and ISOs on the use of dynamic state estimator for enhancement of the reliability, security, and resiliency of electric power systems
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This chapter contains sections titled: Introduction Planning Processes Transmission Limits Decision Support Models Market Efficiency and Transmission Investment Summary Acknowledgments Bibliography
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In this chapter, we present opportunities for improvement in technical modeling of Flexible AC Transmission System (FACTS) devices and Distributed Generation (DG) Technologies for enhancing the efficiency and sustainability of high... more
In this chapter, we present opportunities for improvement in technical modeling of Flexible AC Transmission System (FACTS) devices and Distributed Generation (DG) Technologies for enhancing the efficiency and sustainability of high perform-ance electric power systems. ...
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This paper deals with the application of a mathematical technique named Koopman Mode Analysis in electrical power systems to decompose its swing dynamics into modes of oscillations. A practical data-driven algorithm of the Koopman Mode... more
This paper deals with the application of a mathematical technique named Koopman Mode Analysis in electrical power systems to decompose its swing dynamics into modes of oscillations. A practical data-driven algorithm of the Koopman Mode Analysis is proposed to extract frequencies growth rates and norms of identified modes. The computed Koopman modes are ranked based on their growth rates. The Koopman analysis is applied to a two-area four-machine power system. The choice of the number of KM to be computed is discussed. A comparison of Koopman modes is carried out with linear modes identified by the conventional small-signal stability analysis. This comparison reveals the less damped dynamics which may or not be the linear modes. Such information is particularly useful for power system control in post severe disturbances.
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This paper presents a robust parametric estimation method of the Prony exponential model that is able to suppress white impulsive noise. The method consists of the following steps. Firstly, the Prony parametric estimation problem is... more
This paper presents a robust parametric estimation method of the Prony exponential model that is able to suppress white impulsive noise. The method consists of the following steps. Firstly, the Prony parametric estimation problem is reformulated as a parameter estimation of an Auto-Regressive (AR) model of a known order. Secondly, the outliers of the complex-valued data samples, which are induced by impulsive noise, are identified and suppressed using the iteratively reweighted phase-phase correlator (IPPC); the latter is a robust estimator of correlation for complex-valued Gaussian processes, which has been extended here to handle outliers in the magnitude and in the phase angle of voltage phasor measurements. Finally, the Burg algorithm is applied using a robustly estimated autocorrelation sequence to estimate the AR parameters. The Burg algorithm is chosen over the Yule-Walker technique because it leads to stable AR models even when the processed data samples are of short durations and when the roots of the characteristic polynomial are close to the unit circle, which is precisely the case for power systems with poorly damped excited modes. The good performance of the proposed method is demonstrated on some simulations carried out on the two-area test system. The method is very fast to compute and compatible with real-time application requirements.
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Accurate estimates of the electromechanical disturbance propagation in a power system play an important role in taking preventive, corrective, and in-extremis control actions. This paper proposes an analytical method for disturbance... more
Accurate estimates of the electromechanical disturbance propagation in a power system play an important role in taking preventive, corrective, and in-extremis control actions. This paper proposes an analytical method for disturbance propagation investigation based on the electromechanical wave theory. A frame structure model is developed that allows us to derive an analytical relationship between the disturbance propagation and the turbine-generator inertia, the line reactance, bus voltage, and disturbance source frequency. In addition, the disturbance attenuation and the degree of disturbance propagation are studied. Numerical results performed on a multi-machine chain network and the IEEE 118-bus test system demonstrate the effectiveness of the developed method.
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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... more
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.
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Research Interests: Engineering, Computer Science, Time Series, Electricity Consumption, Autocorrelation, and 14 moreRobustness (evolution), Heteroscedasticity, Gaussian distribution, Missing Values, Load Forecasting, Short Term Load Forecasting, Estimator, Autocorrelation Function, Outlier, Autoregressive model, Forecast Accuracy, Arima Model, maximum likelihood estimate, and Autoregressive Moving Average Model
Critical infrastructures can be regarded as the backbone of the economy of a country since they provide the material support for the delivery of basic services to all the segments of a society. These services include fresh water supply,... more
Critical infrastructures can be regarded as the backbone of the economy of a country since they provide the material support for the delivery of basic services to all the segments of a society. These services include fresh water supply, fuel and electric energy supply, ...