90,651 research outputs found
Online security assessment with load and renewable generation uncertainty: The iTesla project approach
The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (HPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator
Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind
The exceptional benefits of wind power as an environmentally responsible
renewable energy resource have led to an increasing penetration of wind energy
in today's power systems. This trend has started to reshape the paradigms of
power system operations, as dealing with uncertainty caused by the highly
intermittent and uncertain wind power becomes a significant issue. Motivated by
this, we present a new framework using adaptive robust optimization for the
economic dispatch of power systems with high level of wind penetration. In
particular, we propose an adaptive robust optimization model for multi-period
economic dispatch, and introduce the concept of dynamic uncertainty sets and
methods to construct such sets to model temporal and spatial correlations of
uncertainty. We also develop a simulation platform which combines the proposed
robust economic dispatch model with statistical prediction tools in a rolling
horizon framework. We have conducted extensive computational experiments on
this platform using real wind data. The results are promising and demonstrate
the benefits of our approach in terms of cost and reliability over existing
robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Malware still constitutes a major threat in the cybersecurity landscape, also
due to the widespread use of infection vectors such as documents. These
infection vectors hide embedded malicious code to the victim users,
facilitating the use of social engineering techniques to infect their machines.
Research showed that machine-learning algorithms provide effective detection
mechanisms against such threats, but the existence of an arms race in
adversarial settings has recently challenged such systems. In this work, we
focus on malware embedded in PDF files as a representative case of such an arms
race. We start by providing a comprehensive taxonomy of the different
approaches used to generate PDF malware, and of the corresponding
learning-based detection systems. We then categorize threats specifically
targeted against learning-based PDF malware detectors, using a well-established
framework in the field of adversarial machine learning. This framework allows
us to categorize known vulnerabilities of learning-based PDF malware detectors
and to identify novel attacks that may threaten such systems, along with the
potential defense mechanisms that can mitigate the impact of such threats. We
conclude the paper by discussing how such findings highlight promising research
directions towards tackling the more general challenge of designing robust
malware detectors in adversarial settings
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