27,862 research outputs found
Global Linear Complexity Analysis of Filter Keystream Generators
An efficient algorithm for computing lower bounds on the global linear
complexity of nonlinearly filtered PN-sequences is presented. The technique
here developed is based exclusively on the realization of bit wise logic
operations, which makes it appropriate for both software simulation and
hardware implementation. The present algorithm can be applied to any arbitrary
nonlinear function with a unique term of maximum order. Thus, the extent of its
application for different types of filter generators is quite broad.
Furthermore, emphasis is on the large lower bounds obtained that confirm the
exponential growth of the global linear complexity for the class of nonlinearly
filtered sequences
On the number of unsuitable boolean functions in constructions of filter and combining models of stream ciphers
It is well known that every stream cipher is based on a good pseudorandom generator. For cryptographic purposes, we are interested in generation of pseudorandom sequences of the maximal possible period. A feedback register is one of the most known cryptographic primitives that is used in construction of stream generators. We analyze periodic properties of pseudorandom sequences produced by filter and combiner generators equipped with nonlinear Boolean functions. We determine which nonlinear functions in these schemes lead to pseudorandom sequences of not maximal possible period. We call such functions unsuitable and count the exact number of them for an arbitrary n
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
Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian
In this paper the empirical observability Gramian calculated around the
operating region of a power system is used to quantify the degree of
observability of the system states under specific phasor measurement unit (PMU)
placement. An optimal PMU placement method for power system dynamic state
estimation is further formulated as an optimization problem which maximizes the
determinant of the empirical observability Gramian and is efficiently solved by
the NOMAD solver, which implements the Mesh Adaptive Direct Search (MADS)
algorithm. The implementation, validation, and also the robustness to load
fluctuations and contingencies of the proposed method are carefully discussed.
The proposed method is tested on WSCC 3-machine 9-bus system and NPCC
48-machine 140-bus system by performing dynamic state estimation with
square-root unscented Kalman filter. The simulation results show that the
determined optimal PMU placements by the proposed method can guarantee good
observability of the system states, which further leads to smaller estimation
errors and larger number of convergent states for dynamic state estimation
compared with random PMU placements. Under optimal PMU placements an obvious
observability transition can be observed. The proposed method is also validated
to be very robust to both load fluctuations and contingencies.Comment: Accepted by IEEE Transactions on Power System
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