Abstract
This paper presents a new approach for the simultaneous identification of rotor flux components in the rotor reference frame and electrical parameters of a vector controlled induction motor, for real-time implementations, using an extended Kalman filter (EKF) and a reduced order model structure for lower computational effort. The proposed new method requires the measurement of motor speed, stator voltages and currents signals. Using a motor model structure with four electrical parameters, the estimation of flux space phasor and rotor parameters is presented. The estimation is subsequently further extended to include the motor stator parameters and the results are analyzed as well as robustness. Simulated and experimental studies highlight the improvements brought by this new approach, mainly, a simple and reduced state equation, the introduced scalar output equation and lower computational cadency, by using lower sampling frequencies in the proposed rotor reference frame.

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
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.

Abstract
The evaluation of high-potential areas for integrating distributed resources is mainly dependent on the geographical characterization of resources, consumptions, energy technologies and infrastructures. The effect of distributed resources on electric distribution could be studied by a spatial evaluation of incremental costs. This paper presents a Spatial Support System (SSS), based on GIS methodologies, to evaluate the spatial impact caused by integration of distributed power resources on the power distribution network costs. © 2001 IEEE.

Abstract
This text presents an overview of the basic concepts of a Neuro-Fuzzy inference system for spatial Offer-and-Demand forecasting of electric power on a geographical basis, over GIS (Geographical Information Systems).