This paper presents a numerical method for the estimation of induction motor double-cage model parameters from standard manufacturer data: full load mechanical power (rated power), full load reactive electrical power, breakdown torque, and starting current and torque. A model sensitivity analysis for the various electrical parameters shows that stator resistance is the least significant parameter. The nonlinear equations to be solved for the parameters determination are formulated as a minimization problem with restrictions. The method has been tested with 223 motors from different manufacturers, with an average value of the normalized residual error of 1.39/spl middot/10/sup -2/. The estimated parameters of these motors are graphically represented as a function of the rated power.

This paper studies the effects of symmetrical and unsymmetrical voltage sags on three-phase three-legged transformers. The transformer model includes saturation and the parameters have been obtained from experimental measurements. The study shows that sags can produce transformer saturation when voltage recovers. This saturation produces an inrush current that is similar to the inrush current produced during the transformer energizing. The study takes into account that the voltage recovery instant can take only discrete values, since the fault-clearing is produced in the natural current zeroes. The paper also studies the influence of the sag type, duration, depth and fault current angle on the inrush current peak value. The current peak has a periodical dependence on sag duration, and a linear dependence on depth. Unsymmetrical sags can result in current peaks as high as those of symmetrical sags.

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLAB^TM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.

This paper presents a method for squirrel-cage induction motor parameter estimation using a phase-to-phase standstill variable frequency test. The measured resistance and reactance at different frequencies are the data of the minimization error function to be minimized for single- and double-cage model parameters estimation. It is observed that the single-cage model is unable to fit the measured data for frequencies above several tenths of Hertz, whereas the double-cage model fits the measured data accurately in all the frequency ranges (from 0 to 150 Hz). The single- and double-cage estimated parameters are validated by comparison with data from two additional tests: 1) steady-state torque and current measurement test at different speeds; and 2) dynamic free-acceleration test. Again, the agreement between measured and predicted torque (in the first test) and current (in both tests) is satisfactory only for the double-cage model.

The aim of this paper is to analyze the dynamic behavior of the doubly fed induction generator (DFIG) subject to symmetrical voltage sags caused by three-phase faults. A simple control algorithm is considered and assumed ideal: the rotor current in the synchronous reference frame is kept constant. This hypothesis allows the electrical transient to be solved analytically, providing a comprehensive description of DFIG behavior under symmetrical sags. The fault-clearing physics of symmetrical sags is also analyzed. That is, the fault is cleared in the successive natural fault-current zeros, leading to a voltage recovery in one, two, or three steps. This clearing process, called discrete fault clearing in this paper, results in a more accurate sag modeling than the abrupt or instantaneous fault clearing (the usual modeling in the literature). The fault-clearing process has a strong influence on the rotor voltage required to control the rotor current after fault clearing. To compare the effects of both abrupt and discrete sags, different wind turbine (WT) operating points, which determine different generated powers, are considered. This study helps in the understanding of WT fault ride-through capability.