Lamine Mili

Initial review of methods for cascading failure analysis in electric power transmission systems IEEE PES CAMS task force on understanding, prediction, mitigation and restoration of cascading failures

Large blackouts are typically caused by cascading failure propagating through a power system by means of a variety of processes. Because of the wide range of time scales, multiple interacting processes, and the huge number of possible interactions, the simulation and analysis of cascading blackouts is extremely complicated. This paper defines cascading failure for blackouts and gives an initial review of the current understanding, industrial tools, and the challenges and emerging methods of analysis and simulation.

Hypothesis Testing Identification: A New Method For Bad Data Analysis In Power System State Estimation

The anomalous data identification procedures existing today in power system state estimation become problematic-if not totally unefficient-under stringent conditions, such as multiple and interacting bad data. The identification method presented in this paper attempts to alleviate these difficulties. It consists in :(i) computing measurement error estimates and using them as the random variables of concern;(ii) making decisions on the basis of a hypothesis testing which takes into account their statistical properties. Two identification techniques are then derived and further investigated and assessed by means of a realistic illustrative example. Conceptually novel, the identification methodology is thus shown to lead to practical procedures which are efficient, reliable and workable under all theoretically feasible conditions.

Power system and communication network co-simulation for smart grid applications

The vision of a smart grid is predicated upon pervasive use of modern digital communication techniques to todays 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 studied as a single distributed cyber-physical system. This paper proposes a power/network co-simulation framework which integrates power system dynamic simulator and network simulator together using an accurate synchronization mechanism. The accuracy is tunable based on the time-scale requirements of the phenomena being studied. This co-simulation can improve the practical investigation of smart grid and evaluate wide area measurement and control schemes. As a case study an agent-based remote backup relay system is simulated and validated on this co-simulation framework.

GECO: Global Event-Driven Co-Simulation Framework for Interconnected Power System and Communication Network

The vision of a smart grid is predicated upon pervasive use of modern digital communication techniques to todays 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 communication infrastructure mandates that the two systems be studied as a single distributed cyber-physical system. This paper proposes a power system and communication network co-simulation framework (GECO) using a global event-driven mechanism. The accuracy is tunable based on the time-scale requirements of the phenomena being studied. This co-simulation can improve the practical investigation of smart grid and evaluate wide area measurement and control schemes. As a case study, a communication-based backup distance relay protection scheme is co-simulated and validated on this co-simulation framework.

Robust state estimation based on projection statistics [of power systems]

This paper describes a fast and robust 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.

Bad Data Identification Methods In Power System State Estimation-A Comparative Study

The identification techniques available today are first classified into three broad classes. Their behaviour with respect to selected criteria are then explored and assessed. Further, a series of simulations are carried out with various types of bad data. Investigating the way these identification techniques behave allows completing and validating the theoretical comparisons and conclusions.

Least median of squares estimation in power systems

An algorithm for solving LMS (least median of squares) in nonlinear systems has been proposed. The solutions are found through resampling methods based on linear approximations. These methods are suitable for parallel processing. The robustness of the LMS estimator has been verified on several test systems and illustrated on the IEEE 14-bus system. The concept of leverage points shed new light on the meter placement issue as well as the concepts of local redundancy and local breakdown point. A preliminary conclusion is that shorter lines have to be provided with enough measurements in order to increase their local redundancy. Indeed, they tend to be isolated in the factor space and weakly coupled with the surrounding measurements. >

Robust Kalman Filter Based on a Generalized Maximum-Likelihood-Type Estimator

A new robust Kalman filter is proposed that detects and bounds the influence of outliers in a discrete linear system, including those generated by thick-tailed noise distributions such as impulsive noise. Besides outliers induced in the process and observation noises, we consider in this paper a new type called structural outliers. For a filter to be able to counter the effect of these outliers, observation redundancy in the system is necessary. We have therefore developed a robust filter in a batch-mode regression form to process the observations and predictions together, making it very effective in suppressing multiple outliers. A key step in this filter is a new prewhitening method that incorporates a robust multivariate estimator of location and covariance. The other main step is the use of a generalized maximum likelihood-type (GM) estimator based on Schweppes proposal and the Huber function, which has a high statistical efficiency at the Gaussian distribution and a positive breakdown point in regression. The latter is defined as the largest fraction of contamination for which the estimator yields a finite maximum bias under contamination. This GM-estimator enables our filter to bound the influence of residual and position, where the former measures the effects of observation and innovation outliers and the latter assesses that of structural outliers. The estimator is solved via the iteratively reweighted least squares (IRLS) algorithm, in which the residuals are standardized utilizing robust weights and scale estimates. Finally, the state estimation error covariance matrix of the proposed GM-Kalman filter is derived from its influence function. Simulation results revealed that our filter compares favorably with the H¿-filter in the presence of outliers.

Robust state estimation of electric power systems

The exact fit points of the Least Median of Squares (LMS) and the Least Trimmed Squares (LTS) estimators in electric power systems are investigated. The expression of the maximum possible exact fit point /spl delta/*/sub max/ is derived, and the corresponding quantile index /spl nu/ of the ordered squared residual is determined. It is found that /spl delta/*/sub max/ as well as /spl nu/ hinge on the surplus of the network, defined as one less than the smallest number of measurements whose deletion from the data set decreases the rank of the Jacobian matrix. Based on the surplus concept, a system decomposition scheme is developed; it significantly increases the number of outliers that can be handled by the LMS and the LTS estimators. In addition, it dramatically reduces the computing time of these estimators, opening the door to their application in a real-time environment, even for large-scale systems. >

Risk assessment of catastrophic failures in electric power systems

The declining reliability of the US electric power system is raising major concerns among both politicians and power engineers in the USA. One of the reasons put forward by the North Electric Reliability Council (NERC) is the detrimental role played by the protection systems during large disturbances, which tend to help the perturbations to propagate through over-tripping of fault free system components due to hidden failures. It turns out that the present practice in power transmission planning and online security analysis is to neglect the impact of the protection systems. In addition, the aim is to mitigate the vulnerability of the system to the loss of a single piece of equipment only by carrying out an N-1 security analysis. Consequently, the risk of cascading failures leading to blackouts and brownouts is neither assessed nor managed. This paper describes methodologies together with algorithms that assess the conditional risk of catastrophic failures in electric power networks due to hidden failures in protection systems. A catastrophic failure, defined as one that results in the outage of a sizable amount of load, may be caused by dynamic instabilities in the system or exhaustion of the reserves in transmission due to a sequence of line tripping leading to voltage collapse. Only the latter case is being considered. The aim of these algorithms is to identify the weak links in the systems, which are defined as those branches of the network whose tripping due to a fault lead to the highest probabilities of a catastrophic failure. The proposed methods are demonstrated on a 7-bus and a 61-bus system.