10,431 research outputs found
A Tractable Fault Detection and Isolation Approach for Nonlinear Systems with Probabilistic Performance
This article presents a novel perspective along with a scalable methodology
to design a fault detection and isolation (FDI) filter for high dimensional
nonlinear systems. Previous approaches on FDI problems are either confined to
linear systems or they are only applicable to low dimensional dynamics with
specific structures. In contrast, shifting attention from the system dynamics
to the disturbance inputs, we propose a relaxed design perspective to train a
linear residual generator given some statistical information about the
disturbance patterns. That is, we propose an optimization-based approach to
robustify the filter with respect to finitely many signatures of the
nonlinearity. We then invoke recent results in randomized optimization to
provide theoretical guarantees for the performance of the proposed filer.
Finally, motivated by a cyber-physical attack emanating from the
vulnerabilities introduced by the interaction between IT infrastructure and
power system, we deploy the developed theoretical results to detect such an
intrusion before the functionality of the power system is disrupted
Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks
Kalman filters and observers are two main classes of dynamic state estimation
(DSE) routines. Power system DSE has been implemented by various Kalman
filters, such as the extended Kalman filter (EKF) and the unscented Kalman
filter (UKF). In this paper, we discuss two challenges for an effective power
system DSE: (a) model uncertainty and (b) potential cyber attacks. To address
this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced
and implemented. Various Kalman filters and the observer are then tested on the
16-machine, 68-bus system given realistic scenarios under model uncertainty and
different types of cyber attacks against synchrophasor measurements. It is
shown that CKF and the observer are more robust to model uncertainty and cyber
attacks than their counterparts. Based on the tests, a thorough qualitative
comparison is also performed for Kalman filter routines and observers.Comment: arXiv admin note: text overlap with arXiv:1508.0725
Recommendations and illustrations for the evaluation of photonic random number generators
The never-ending quest to improve the security of digital information
combined with recent improvements in hardware technology has caused the field
of random number generation to undergo a fundamental shift from relying solely
on pseudo-random algorithms to employing optical entropy sources. Despite these
significant advances on the hardware side, commonly used statistical measures
and evaluation practices remain ill-suited to understand or quantify the
optical entropy that underlies physical random number generation. We review the
state of the art in the evaluation of optical random number generation and
recommend a new paradigm: quantifying entropy generation and understanding the
physical limits of the optical sources of randomness. In order to do this, we
advocate for the separation of the physical entropy source from deterministic
post-processing in the evaluation of random number generators and for the
explicit consideration of the impact of the measurement and digitization
process on the rate of entropy production. We present the Cohen-Procaccia
estimate of the entropy rate as one way to do this. In order
to provide an illustration of our recommendations, we apply the Cohen-Procaccia
estimate as well as the entropy estimates from the new NIST draft standards for
physical random number generators to evaluate and compare three common optical
entropy sources: single photon time-of-arrival detection, chaotic lasers, and
amplified spontaneous emission
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