Lamine Mili
Virginia Tech, Bradley Dept of Electrical and Computer Engineering, Faculty Member
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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 Modeling edit
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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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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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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, ...
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Traditional state estimators require longer scan time, leading to delayed, and inaccurate state estimation. Given the increased deployment trend of phasor measurement units (PMUs), it is expected that all-PMU state estimation will... more
Traditional state estimators require longer scan time, leading to delayed, and inaccurate state estimation. Given the increased deployment trend of phasor measurement units (PMUs), it is expected that all-PMU state estimation will eventually replace traditional or mixed state estimators at the control centers of power utilities. Due to the repeated calibration of the voltage and current transformers at the measurement sites, and direct time-synchronized measurement of phasors, the estimated state by an all-PMU state estimator is not only accurate, but also available at a rapid rate, leading to the use of the system state for protection, stabilization, and even calibration of the measuring devices. However, due to high reliance on an advanced communication network infrastructure for the delivery of large amount of measurements in real-time, the cyber attack surface of the power system is increased. Deliberate cyber attacks or unintentional network failures can affect the state estimator leading to misoperations of the power system. In this paper, we study the cyber security impacts on the all-PMU state estimator, using a power system and data network co-simulation method. A linear state estimator for a model of the New England 39-bus system and the corresponding data network is built in a global event-driven co-simulation platform “GECO” which was developed and leveraged for our experimental setup. The co-simulation of PSLF (power system simulator) and NS-2 (network simulator) is run with injection of attacks on the network. The injected cyber attacks in the form of network failures or malicious data injection are simulated and their effects are observed. We also, observe the robustness of the all-PMU state estimator, when the number of affected measurements is below a threshold.
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Abstract The vision of a smart grid is predicated upon pervasive use of modern digital communication techniques to today's power system. As wide area measurements and control techniques are... more
Abstract The vision of a smart grid is predicated upon pervasive use of modern digital communication techniques to today's power system. As wide area measurements and control techniques are being developed and deployed for a more resilient power system, the role of communication network is becoming prominent. Power system dynamics gets influenced by the communication delays in the network. Therefore, extensive integration of power system and its communication infrastructure mandates that the two systems are ...
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Computer Science, Power System, Agent Based, Digital Communication, Cyber Physical Systems, and 15 moreNetwork Simulation, Case Study, Power Grid, Electric Power System, Discrete Event Simulation, Community Networks, Backup, RELAY, Grid Applications, Network Simulator, Power System Dynamics, Power System Simulation, Communication Delay, Simulation Framework, and Communications System
This chapter contains sections titled: Introduction Structure of the Next Generation Optimization Foundations of the Next Generation Optimization Applications of Next Generation Optimization to Power Systems Grand Challenges in Next... more
This chapter contains sections titled: Introduction Structure of the Next Generation Optimization Foundations of the Next Generation Optimization Applications of Next Generation Optimization to Power Systems Grand Challenges in Next Generation Optimization and Research Needs Concluding Remarks and Benchmark Problems Acknowledgments Bibliography
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Mathematics, Computer Science, Modeling, Forecasting, Multidisciplinary, and 15 moreEstimation Theory, French, Gaussian processes, Electricity Consumption, Implementation, Gaussian, Moving average, Francais, Missing Values, Load Forecasting, Gaussian Process, ARMA model, Autocorrelation Function, Breakdown Point, and Autoregressive model
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It has been observed that the French electric load series possesses outliers and breaks. Outliers are deviant data points while breaks are lasting abrupt changes in the stochastic pattern of the series. It turns out that outliers and... more
It has been observed that the French electric load series possesses outliers and breaks. Outliers are deviant data points while breaks are lasting abrupt changes in the stochastic pattern of the series. It turns out that outliers and breaks significantly degrade the reliability and accuracy of conventional day-ahead estimation and forecasting methods. Robust methods are needed for this application. In
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Abstract - Electric power industry deregulation has transformed state estimation from an important application into a critical one. State estimations in Power systems involve very tedious computations since large systems can consist of... more
Abstract - Electric power industry deregulation has transformed state estimation from an important application into a critical one. State estimations in Power systems involve very tedious computations since large systems can consist of thousands of buses and lines. In this paper, we apply ...
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Engineering, Computer Science, Logic, Biomedical Engineering, Power System, and 15 moreCommunication Networks, Power System Protection, Communication System, Failure Analysis, Communication Network, Disaster, Power Engineering, Frequency, Countermeasures, Cascading, Community Networks, Electrical And Electronic Engineering, Data Gathering, Catastrophic failure, and Real Time Data
AbstractIn this paper, the stochastic characteristics of the elec-tric consumption in France are analyzed. It is shown that the load time series exhibit lasting abrupt changes in the stochastic pat-tern, termed breaks. The goal is to... more
AbstractIn this paper, the stochastic characteristics of the elec-tric consumption in France are analyzed. It is shown that the load time series exhibit lasting abrupt changes in the stochastic pat-tern, termed breaks. The goal is to propose an efficient and robust load forecasting ...
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Computer Science, Multivariate Statistics, Time series analysis, Exponential Smoothing, Robustness (evolution), and 9 moreElectric Power System, Prediction Model, Heteroscedasticity, Mathematical Model, Load Forecasting, Short Term Load Forecasting, Estimator, Electrical And Electronic Engineering, and Outlier
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Engineering, Civil Engineering, Computer Science, Risk assessment, Power System Protection, and 15 moreReliability Engineering, Int, Power Transmission, Security Analysis, Critical Infrastructures, Electric Power System, Risk Assessment, Electric Power, Blackout, Electric Power Transmission, Cascading Failure, Voltage Collapse, Tripping, Catastrophic failure, and Cascading failures
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Electricity markets have emerged all around the world since the early 1990s. In general, they tend to be characterized by an oligopoly of generators, very little demand - side elasticity in the short term, and complex administered market... more
Electricity markets have emerged all around the world since the early 1990s. In general, they tend to be characterized by an oligopoly of generators, very little demand - side elasticity in the short term, and complex administered market mecha-nisms. The market mechanisms are ...
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Abstract Thii paper considers the effect of non-gaussian noise on the conventional estimate of cyclic correla-tion. It is shown that noise having a distribution function with heavier tails than the gaussian slows the convergence of the... more
Abstract Thii paper considers the effect of non-gaussian noise on the conventional estimate of cyclic correla-tion. It is shown that noise having a distribution function with heavier tails than the gaussian slows the convergence of the estimate to the expected value. Al-ternative ...
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Abstract In this paper an enhanced power flow algorithm in complex plane is proposed. The power flow models in complex plane is naturally developed in Cartesian coordinates, thus most of the constraints equations can be written as... more
Abstract In this paper an enhanced power flow algorithm in complex plane is proposed. The power flow models in complex plane is naturally developed in Cartesian coordinates, thus most of the constraints equations can be written as quadratic functions. Consequently, the Taylor series expansion stops in the third term and the exact nonlinearity of the quadratic complex power flow equations is retained while the remaining functions are dealt through the Newton-Raphson method. Minor changes in the codes are required to transform the Newton-Raphson method into the enhanced power flow approach in complex plane. The new algorithm exihibits either a superior behavior in well- or ill-conditioned networks. The features and advantages of the proposed algorithm are illustrated through a small example and case studies carried out either on the well- or ill-conditioned fashion of the IEEE-14, −30, −57 and −118 bus and the Brazilian Southern-equivalent of 1916-buses, termed as SIN-1916.