227,964 research outputs found

    Noise Resilient Learning for Attack Detection in Smart Grid Pmu Infrastructure

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    Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the effect of false alarms caused by the noise. Specifically, we propose a feature extraction framework that uses some Pythagorean Means of the active power from a cluster of PMUs, reducing multi-dimensional nature of the PMU data streams via quick big data summarization. We also propose a robust and noise resilient methodology for learning thresholds based on generalized robust estimation theory of our invariant feature. We experimentally validate our approach and demonstrate improved reliability performance using two completely different datasets collected from real distribution level PMU infrastructures

    Dynamically adaptive partition-based interest management in distributed simulation

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    Performance and scalability of distributed simulations depends primarily on the effectiveness of the employed interest management (IM) schema that aims at reducing the overall computational and messaging effort on the shared data to a necessary minimum. Existing IM approaches, which are based on variations or combinations of two principle data distribution techniques, namely region-based and grid-based techniques, perform poorly if the simulation develops an overloaded host. In order to facilitate distributing the processing load from overloaded areas of the shared data to less loaded hosts, the partition-based technique is introduced that allows for variable-size partitioning the shared data. Based on this data distribution technique, an IM approach is sketched that is dynamically adaptive to access latencies of simulation objects on the shared data as well as to the physical location of the objects. Since this re-distribution is decided depending on the messaging effort of the simulation objects for updating data partitions, any load balanced constellation has the additional advantage to be of minimal overall messaging effort. Hence, the IM schema dynamically resolves messaging overloading as well as overloading of hosts with simulation objects and therefore facilitates dynamic system scalability

    ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids

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    Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections

    Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells

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    Significant efforts have been made and are still being made on short-term traffic prediction methods, specially for highway traffic based on punctual measurements. Literature on predicting the spatial distribution of the traffic in urban intersections is, however, very limited. This work presents a novel data-driven prediction algorithm based on Random Forests regression over spatio-temporal aggregated data of vehicle counts inside a grid. The proposed approach aims to estimate future distribution of V2X traffic demand, providing a valuable input for a dynamic management of radio resources in small cells. Radio Access Networks (RAN) working in the terahertz band and deployed in small cells are expected to meet the high-demanding data rate requirements of connected vehicles. However, terahertz frequency propagation has important limitations in outdoor scenarios, including distance propagation, high absorption coefficients values and low reflection properties. More concretely, in settings such as complex road intersections, dynamic signal blockage and shadowing effects may cause significant power losses and compromise the quality of service for some vehicles. The forthcoming network demand, estimated from the regression algorithm is used to compute the losses expected due to other vehicles potentially located between the transmitter and the receiver. We conclude that our approach, which is designed from a grid-like perspective, outperforms other traffic prediction methods and the combined result of these predictions with a dynamic reflector orientation algorithm, as a use case application, allows reducing the ratio of vehicles that do not receive a minimum signal power

    Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

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    The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies
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