Abstract
In this paper a new method of estimation of the induction motor electric parameters is presented and discussed. The proposed method uses the indirect approach that consists in estimating the continuous-time parameters by first fitting a discrete-time model to the data and then converting this model to a continuous-time version. The main contribution is that by using a polynomial pre-filter it is possible to compute the induction motor equivalent circuit parameters via an inverse transformation between the discrete-time and the continuous-time models. The procedure is potentially useful for the design of self-commissioning drives and may provide initial estimates to extended Kalman filtering type of procedures. The effectiveness of the proposed method is verified by simulated tests and results of the method are discussed.

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.

Abstract
In this paper we describe two approaches developed by two research teams to address the topology identification problem in the scope of state estimation. Both approaches aim at enlarging the traditional concept of strict state estimation, assuming that the network topology is pre-determined and is fixed. In fact, we are generalizing state estimation, enlarging its domain and aiming at obtaining topology information from a state estimation run. Apart from the description of those two techniques, the paper includes a'set of tests performed over the same test system in order to illustrate the interest of the approaches and to evaluate their performances. © 2001 IEEE.

Abstract
Distributed Generation (DG) facilities require, like other energy projects, a sitting review process to acquire the permits and approval needs for construction and operation. In this process different groups and individuals with different roles, interests and priorities are involved. This paper presents a Spatial Decision Support System (SDSS) that helps to identify permissible areas to install DG facilities. Wind energy facilities are used in this paper to exemplify the use of the SDSS. © 2001 IEEE.