79,872 research outputs found
Local Cyber-Physical Attack for Masking Line Outage and Topology Attack in Smart Grid
Malicious attacks in the power system can eventually result in a large-scale
cascade failure if not attended on time. These attacks, which are traditionally
classified into \emph{physical} and \emph{cyber attacks}, can be avoided by
using the latest and advanced detection mechanisms. However, a new threat
called \emph{cyber-physical attacks} which jointly target both the physical and
cyber layers of the system to interfere the operations of the power grid is
more malicious as compared with the traditional attacks. In this paper, we
propose a new cyber-physical attack strategy where the transmission line is
first physically disconnected, and then the line-outage event is masked, such
that the control center is misled into detecting as an obvious line outage at a
different position in the local area of the power system. Therefore, the
topology information in the control center is interfered by our attack. We also
propose a novel procedure for selecting vulnerable lines, and analyze the
observability of our proposed framework. Our proposed method can effectively
and continuously deceive the control center into detecting fake line-outage
positions, and thereby increase the chance of cascade failure because the
attention is given to the fake outage. The simulation results validate the
efficiency of our proposed attack strategy.Comment: accepted by IEEE Transactions on Smart Grid. arXiv admin note: text
overlap with arXiv:1708.0320
Accelerating scientific codes by performance and accuracy modeling
Scientific software is often driven by multiple parameters that affect both
accuracy and performance. Since finding the optimal configuration of these
parameters is a highly complex task, it extremely common that the software is
used suboptimally. In a typical scenario, accuracy requirements are imposed,
and attained through suboptimal performance. In this paper, we present a
methodology for the automatic selection of parameters for simulation codes, and
a corresponding prototype tool. To be amenable to our methodology, the target
code must expose the parameters affecting accuracy and performance, and there
must be formulas available for error bounds and computational complexity of the
underlying methods. As a case study, we consider the particle-particle
particle-mesh method (PPPM) from the LAMMPS suite for molecular dynamics, and
use our tool to identify configurations of the input parameters that achieve a
given accuracy in the shortest execution time. When compared with the
configurations suggested by expert users, the parameters selected by our tool
yield reductions in the time-to-solution ranging between 10% and 60%. In other
words, for the typical scenario where a fixed number of core-hours are granted
and simulations of a fixed number of timesteps are to be run, usage of our tool
may allow up to twice as many simulations. While we develop our ideas using
LAMMPS as computational framework and use the PPPM method for dispersion as
case study, the methodology is general and valid for a range of software tools
and methods
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
In weather forecasting, nonhomogeneous regression is used to statistically
postprocess forecast ensembles in order to obtain calibrated predictive
distributions. For wind speed forecasts, the regression model is given by a
truncated normal distribution where location and spread are derived from the
ensemble. This paper proposes two alternative approaches which utilize the
generalized extreme value (GEV) distribution. A direct alternative to the
truncated normal regression is to apply a predictive distribution from the GEV
family, while a regime switching approach based on the median of the forecast
ensemble incorporates both distributions. In a case study on daily maximum wind
speed over Germany with the forecast ensemble from the European Centre for
Medium-Range Weather Forecasts, all three approaches provide calibrated and
sharp predictive distributions with the regime switching approach showing the
highest skill in the upper tail
Multiple-F0 estimation of piano sounds exploiting spectral structure and temporal evolution
This paper proposes a system for multiple fundamental frequency estimation of piano sounds using pitch candidate selection rules which employ spectral structure and temporal evolution. As a time-frequency representation, the Resonator Time-Frequency Image of the input signal is employed, a noise suppression model is used, and a spectral whitening procedure is performed. In addition, a spectral flux-based onset detector is employed in order to select the steady-state region of the produced sound. In the multiple-F0 estimation stage, tuning and inharmonicity parameters are extracted and a pitch salience function is proposed. Pitch presence tests are performed utilizing information from the spectral structure of pitch candidates, aiming to suppress errors occurring at multiples and sub-multiples of the true pitches. A novel feature for the estimation of harmonically related pitches is proposed, based on the common amplitude modulation assumption. Experiments are performed on the MAPS database using 8784 piano samples of classical, jazz, and random chords with polyphony levels between 1 and 6. The proposed system is computationally inexpensive, being able to perform multiple-F0 estimation experiments in realtime. Experimental results indicate that the proposed system outperforms state-of-the-art approaches for the aforementioned task in a statistically significant manner. Index Terms: multiple-F0 estimation, resonator timefrequency image, common amplitude modulatio
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