A stable electricity supply is vitalfor modern society. used to distribute power evenly through the entire system, However, many parts of our power transmission grid are operating however, due to deregulation there is incentive for transmission near their operational limits. Such stressed systems are vulnerable operators to operate as near capacity as possible. This allows to cascading failures, where a few small faults can induce a operator to o e anrcapct as possiblets al cascade of failures potentially leading to a major blackout. The excess power to be purchased from distant markets, but at Unified Power Flow Controller (UPFC), the most powerful high-the expense of reduced system stability margins. For the speed, semi-conductor based power flow device, can be used as foreseeable future the power industry will be able to produce a theoretical model to study how these devices can be used to enough power to meet customer demands, however, the current improve power grid resilience. The blueprint presented here can be transmission grid may be operating so close to its limits that used to iteratively identify critical weaknesses in power grids and to recommend a means offixing these weaknesses via the installation ansmalsful coudcaueawblacutw [7].
The world is increasingly dependent on critical infrastructures such as the electric power grid, water, gas, and oil transport systems, which are susceptible to cascading failures that can result from a few faults. Due to the combinatorial complexity in the search spaces involved, most traditional search techniques are inappropriate for identifying these faults and potential protections against them. This paper provides a computational methodology employing competitive coevolution to simultaneously identify low-effort, high-impact faults and corresponding means of hardening infrastructures against them. A power system case study provides empirical evidence that our proposed methodology is capable of identifying cost effective modifications to substantially improve the fault tolerance of critical infrastructures. I. General MethodologyThe world is increasingly dependent on critical infrastructures such as the electric power grid, water, gas and oil transport systems. At the same time, these infrastructures are increasingly susceptible to faults in the form of natural disasters and intentional disruption. Due to both increasing demand, which is outpacing infrastructure expansion, and the increasingly interconnected nature of infrastructures, many critical infrastructures are becoming vulnerable to cascading failures, where a fault caused by an external force may induce a domino-effect of component failures.These trends combined raise the specter of a well targeted attack bringing down an entire system of interconnected infrastructures, resulting in a devastating economic blow and potentially a significant loss of life. An important implication is that traditional infrastructure risk analysis methods, often relying on Monte Carlo sampling of disaster scenarios, are no longer sufficient. Instead, systematic analysis based on worst-case attacks by intelligent adversaries is essential.The addition of control devices, such as adding a pump in a water system, coupled with intelligent control algorithms allow the effective use of spare system capacity. When working together, multiple control devices have the potential to protect components by better use of underutilized areas. The ability to balance system use in such a manner presents a means to stop cascading failures as well as better utilize system resources during normal operating conditions.The problems of finding optimally balanced hardenings and worst-case fault scenarios are interdependent and their solution spaces share the characteristics of combinatorial complexity, making exhaustive search infeasible, and nonlinear dependencies between solution components, resulting in many local optima which defeat most traditional search algorithms. This paper provides a methodology, based on competitive coevolutionary algorithms inspired by Neo-Darwinian arms races, to simultaneously evolve near optimal hardenings and low-cost, high-impact faults. The effectiveness of our methodology is demonstrated on a power system case study. Competitive CoevolutionEvolutionary Algorith...
Abstract-Increasing demand coupled with limitations on new construction indicate that existing power transmission must be better controlled in order to continue reliable operation. Recent advances in FACTS devices provide a mechanism to better control power flow on the transmission network. One particular device, the Unified Power Flow Controller (UPFC), holds the most promise for maintaining operation even when the system has suffered partial failure (either naturally occurring, due to human error, or a malicious attack). In addition to the capital cost, the primary obstacles to widespread UPFC use are the combined problems of selecting the most cost effective locations for installation and maintaining proper control of them once installed.In this paper we list evidence that Gradient Descent search based on load-flow computation is more realistic and accurate than many of the optimization techniques currently in use. We then demonstrate that Gradient Descent search can be used to select control points that improve system fault tolerance more than those found by the Max-Flow technique. In addition, we demonstrate that the size of the system being computed and the number of computations is bounded and is practical for real time control.
High-speed simulations of power transmission systems, which often rely on solving nonlinear systems of equations, are an increasingly important tool for training, testing equipment, on-line control and situational awareness. Such simulations, however, suffer from two major problems: (1) they can be too computationally demanding to simulate large, complex systems within appropriate time constraints; and (2) they are difficult to develop and debug. Prior work has shown how symbolic computation can be used to help reduce both problems. In this paper, we: (1) review common concepts in power system simulations; (2) summarize prior use of symbolic computation in power system simulation; (3) explore the advantages and disadvantages achieved via symbolic techniques; (4) extend the techniques to solve linear systems via a priori symbolic LU decomposition; and (5) demonstrate the advantages of symbolic techniques on a transient event simulation of the IEEE 118-bus test power system, which runs in one-tenth the time of an equivalent traditional (sparse matrix) approach.
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