We propose a statistical algorithm for detecting line outages in a power system and show that it has better performance than other schemes proposed in the literature. Our algorithm is based on the Cumulative Sum (CuSum) test from the Quickest Change Detection (QCD) literature. It exploits the statistical properties of the measured voltage phase angles before, during, and after a line outage, whereas other methods in the literature only utilize the change in statistics that occurs at the instant of outage.

A method to detect and isolate power system transmission line outages in near real-time is proposed. In particular, a linearized power system model is presented and a statistical model for line outage detection and isolation is developed using this model. To detect and isolate the line outage quickly, algorithms based on statistical quickest change detection are employed.

The sixteen articles in this special section explore advances in communication technologies that have the potential for improving energy efficiency and realizing the smart grid vision. The articles tackle important challenges with respect to smart grid communications and include a wide range of topics such as electric load models, electric vehicle charging, models for demand response, power management optimization and storage, pricing, and security.

This paper proposes a set-theoretic method to capture the effect of parametric uncertainty in reliability and performability indices obtained from Markov reliability and reward models. We assume that model parameters, i.e., component failure and repair rates, are not perfectly known, except for upper and lower bounds obtained from engineering judgment or field data. Thus, the values that these parameters can take are constrained to lie within a set. In our method, we first construct a minimum volume ellipsoid that upper bounds this set, and hence contains all possible values that the parameters can take. This ellipsoid is then propagated via set operations through a second-order Taylor series expansion of the Markov chain stationary distribution, resulting in a set that provides approximate bounds on reliability and performability indices of interest. Case studies pertaining to a two-component shared load system with common-cause failures, and preventative maintenance of an electric-power distribution transformer, are presented.

This paper considers networked systems and develops distributed algorithm that is resilient against potential packet drops in the communication links between system components. We apply this algorithm to the problem of coordinating distributed energy resources (DERs) for the provision of ancillary services in electrical networks, e.g., reactive power support for voltage control. In this problem, each system component can contribute a certain amount of active and/or reactive power, bounded from above and (possibly) below by capacity constraints, and the objective is to coordinate the components so as to collectively provide a predetermined total amount of active and/or reactive power. In the algorithm we propose to address this problem, each DER maintains a set of variables and updates them through information exchange with neighboring DERs. We show that, as long as the underlying graph that describes the information exchange between components is strongly connected, and the predetermined total amount of active and/or reactive power does not violate (upper or lower) total capacity constraints, DERs can use this approach to calculate, in a distributed fashion, their fair contribution (subject to their capacity constraints). We show that the proposed algorithms reach almost surely convergence to the fair solution, even in the presence of communication link failures.

A common concern with Markov reliability and reward models is that model parameters, i.e., component failure and repair rates, are seldom perfectly known. This paper proposes a numerical method based on the Taylor series expansion of the underlying Markov chain stationary distribution (associated to the reliability and reward models) to propagate parametric uncertainty to reliability and performability indices of interest. The Taylor series coefficients are expressed in closed form as functions of the Markov chain generator-matrix group inverse. Then, to compute the probability density functions of the reliability and performability indices, random variable transformations are applied to the polynomial approximations that result from the Taylor series expansion. Additionally, closed-form expressions that approximate the expectation and variance of the indices are also derived. A significant advantage of the proposed framework is that only the parametrized Markov chain generator matrix is required as an input, i.e., closed-form expressions for the reliability and performability indices as a function of the model parameters are not needed. Several case studies illustrate the accuracy of the proposed method in approximating distributions of reliability and performability indices. Additionally, analysis of a large model demonstrates lower execution times compared to Monte Carlo simulations.

We discuss the concept of input-to-state stability in dynamical systems and its application to address the reachability problem in power systems, i.e., what are the bounds on all possible system trajectories given certain parameters are unknown but bounded. We illustrate the application of the concept with a single-machine infinite-bus system where the infinite bus voltage is not perfectly known except for an upper and lower bound.

This paper proposes a fault coverage model for linear time-invariant (LTI) systems subject to uncertain input. A state-space representation, defined by the state-transition matrix, and the input matrix, is used to represent LTI system dynamic behavior. The uncertain input is considered to be unknown but bounded, where the bound is defined by an ellipsoid. The state-transition matrix, and the input matrix must be such that, for any possible input, the system dynamics meets its intended function, which can be defined by some performance requirements. These performance requirements constrain the system trajectories to some region of the state-space defined by a symmetrical polytope. When a fault occurs, the state-transition matrix, and the input matrix might be altered; and then, it is guaranteed the system survives the fault if all possible post-fault trajectories are fully contained in the region of the state-space defined by the performance requirements. This notion of guaranteed survivability is the basis to model (in the context of LTI systems) the concept of fault coverage, which is a probabilistic measure of the ability of the system to keep delivering its intended function after a fault. Analytical techniques to obtain estimates of the proposed fault coverage model are presented. To illustrate the application of the proposed model, two examples are discussed.