Control of chaos: Methods and applications in engineering

This paper provides a comprehensive list of books, reports, workshops and technical papers related to voltage stability and security

Bibliography on voltage stability

The availability of parallel processing hardware and software presents an opportunity and a challenge to apply this new computation technology to solve power system problems. The allure of parallel processing is that this technology has the potential to be cost effectively used on computationally intense problems. The objective of this paper is to define the state of the art and identify what the authors see to be the most fertile grounds for future research in parallel processing as applied to power system computation. As always, such projections are risky in a fast changing field, but the authors hope that this paper will be useful to the researchers and practitioners in this growing area.

Parallel processing in power systems computation

In this brief, stability of multiple equilibria of recurrent neural networks with time-varying delays and the piecewise linear activation function is studied. A sufficient condition is obtained to ensure that n-neuron recurrent neural networks can have (4k-1)n equilibrium points and (2k)n of them are locally exponentially stable. This condition improves and extends the existing stability results in the literature. Simulation results are also discussed in one illustrative example.

Multistability of Recurrent Neural Networks With Time-varying Delays and the Piecewise Linear Activation Function

This paper presents an approach for generating new hyperchaotic attractors in a ring of Chua's circuits. By taking a closed chain of three circuits and exploiting sine functions as nonlinearities, the proposed technique enables 3D-scroll attractors to be generated. In particular, the paper shows that 3D-scroll dynamics can be designed by modifying six parameters related to the circuit nonlinearities.

New 3d-scroll attractors in hyperchaotic chua's circuits forming a ring

A synthesis procedure for associative memories using Discrete-Time Cellular Neural Networks (DTCNN's) with learning and forgetting capabilities is presented. The proposed design technique generates networks with the capability of learning new patterns and forgetting old ones without recomputing the whole interconnection matrix and the input vector. >

Discrete-time cellular neural networks for associative memories with learning and forgetting capabilities

A parallel method for the transient stability simulation of power systems is presented. The trapezoidal rule is used to discretize the set of algebraic-differential equations which describes the transient stability problem. A parallel Block-Newton relaxation technique is used to solve the overall set of algebraic equations concurrently on all the time steps. The parallelism in space of the problem is also exploited. Furthermore, the parallel-in-time formulation is used to change the time steps between iterations by a nested iteration multigrid technique, in order to enhance the convergence of the algorithm. The method has the same reliability and model-handling characteristics of typical dishonest Newton-like procedures. Test results on realistic power systems are presented to show the capability and usefulness of the suggested technique. >

A Gauss-Jacobi-Block-Newton method for parallel transient stability analysis (of power systems)

In this paper the Carroll–Pecora concept of chaotic system synchronization is extended to the synchronization of hyperchaotic circuits. A circuit exhibiting a hyperchaotic behavior is duplicated to generate two cascaded response subsystems, properly driven by synchronizing signals. Conditions for achieving synchronization are illustrated in detail. The proposed method is applied to two identical unidirectionally coupled hyperchaotic Chua’s circuits.

a Method for the Synchronization of Hyperchaotic Circuits

In this paper a synthesis technique for the synchronization of hyperchaotic circuits with application to secure communications is developed. It is well known that information-bearing signals can be considered as causes of perturbations, when a secure communication system based on a masking technique is implemented. Starting from this consideration, in this paper a feedback control scheme is developed to guarantee synchronization between transmitter and receiver. The control scheme involves as many state variables in the feedback as the number of information signals to be transmitted. After deriving the nonlinear dynamics of the synchronization error system, the feedback matrix is determined by finding a suitable Lyapunov function and by imposing the conditions which assure the global asymptotic stability of the origin of the error system. The method is illustrated by considering hyperchaotic circuits constituted by coupled Chua's oscillators, for which analytic expressions of the feedback gains are derived.

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Synchronization of hyperchaotic circuits via continuous feedback control with application to secure communications

In this paper a global design method for associative memories using discrete-time cellular neural networks (DTCNNs) is presented. The proposed synthesis technique enables to realize associative memories with several advantageous features. First of all, grey-level as well as bipolar images can be stored. Moreover, the proposed approach generates networks with learning and forgetting capabilities. Finally, it is possible to design networks with any kind of predetermined interconnection structure. In particular, neighbourhoods without line crossings can be chosen, greatly simplifying the VLSI implementation of the designed DTCNNs.

In the first part of this work a model of a multilevel threshold network is presented and a stability analysis is carried out using basic notions deriving from non-linear dynamical system theory. The synthesis procedure is then developed by means of a pseudoinversion technique, assuring learning and forgetting capabilities of the designed DTCNN. The use of a neighbourhood without line crossings is also discussed. Simulation results are reported to show the capability of the proposed approach.

M. Brucoli

Papers

Discrete-time cellular neural networks for associative memories with learning and forgetting capabilities

A Gauss-Jacobi-Block-Newton method for parallel transient stability analysis (of power systems)

a Method for the Synchronization of Hyperchaotic Circuits

Synchronization of hyperchaotic circuits via continuous feedback control with application to secure communications

A global approach to the design of discrete-time cellular neural networks for associative memories