Abstract
Due to their efficiency and their interesting design and implementation features, metaheuristics have been used for a long time with success, in dealing with combinatorial problems. In recent years they have been applied to the Unit Commitment problem with rather interesting results that justify further research in the area. In this paper we present a Simulated Annealing approach to the Unit Commitment problem. Two coding schemes are compared, new neighbourhood structures are presented and some searching strategies are discussed. Preliminary computational experience, performed on some test instances, shows that this approach is flexible, effective and able to handle variations on the problem structure. © 2001 IEEE.

Abstract
Very often, in dependability evaluation, the systems under study are assumed to have a Markovian behavior. This assumption highly simplifies the calculations, but introduces significant errors when the systems contain deterministic or quasi-deterministic processes, as it often happens with industrial systems. Existing methodologies for non-Markovian systems, such as device stage method [1], the supplementary variables method or the imbedded Markov chain method [2] do not provide an effective solution to deal with this class of systems, since their usage is restricted to relatively simple and small systems. This paper presents an analytical methodology for the dependability evaluation of non-Markovian discrete state systems, containing both stochastic and deterministic processes, along with an associated systematic resolution procedure suitable for numerical processing. The methodology was initially developed in the context of a research work [3] addressing the dependability modeling, analysis and evaluation of large industrial information systems. This paper, extends the application domain to the evaluation of reliability oriented indexes and to the assessment of multiple components systems. Examples will be provided throughout the paper, in order to illustrate the fundamental concepts of the methodology, and to demonstrate its practical usefulness.

Abstract
The 2001 PowerTech Conference held in Porto, Portugal from 10 to 13 September, provided an international forum for participants to share knowledge, experiences, and new ideas about the changes in the electronic power sector. The Conference was attended by more than 450 delegates from 50 countries.

Abstract
Voltage stability is an important concern of power system managers not only in the net planning phase but also in operation. This issue has become especially critical in recent years due to the deregulation phenomenon because of new exploration policies complying a system operation closer to its security limits. In particular, voltage collapse distance may approach emergency values or, in the worst case, make the system collapse. As voltage profile is extremely dependent on reactive power compensation, most common approaches integrate both objectives in the operation setting phase, trying to optimize reactive power production taking voltage profile into consideration. In this paper, authors propose an evolutionary approach application to the same problem but in the planning phase. It is shown that the cooperative procedure of planning and preventive control provides better solutions that if one deals with these issues one at a time. © 2001 IEEE.

Abstract
This paper deals with a problem of identification of the best subset of variables that should be used for dynamic security assessment of a power system, when this task is pro-vided by artificial neural networks (ANN)- The approach de-scribed here exploits ANN output sensitivities relatively to the inputs and correlation degrees, to identify the most relevant system variables to be used for an effective security assessment task. The ANNs are initially trained with all low-correlated candidate features, which enables the sensitivity analyses for the initial set of system features. Derivatives of the ANN output relatively to each input are obtained by exploiting the chain rule, similar to the one used for weights adaptation on Back-propagation Algorithm. A description of the application of this approach in a real system is present in the paper. Results obtained in the dynamic security assessment problem of the network of the island of Crete were quite successful. © 2001 IEEE.

Abstract
Feature subset selection is a central issue in a vast diversity of problems including classification, function approximation, machine learning and adaptive control. On a wide variety of applications, especially when using real data, input features may be not independent and output variable depends on the relationship among inputs rather than on input values themselves. Feature selection methods that assume independence of attributes will fail on these cases. On the other side, most of alternative approaches are quasi-exhaustive, requiring large CPU processing time. In this paper, an alternative methodology based on sensitivity analysis of trained artificial neural networks (ANN) is analyzed. Results so far attained on illustrative toy examples and on real data support the validity of the developed approach.