Dynamic Stability Enhancement of Power Systems Using Neural-Network Controlled Static-Compensator

This paper aims at enhancement of dynamic stability of power systems using artificial neural network (ANN) controlled static VAR compensator (SVC). SVC is proven the fact that it improves the dynamic stability of power systems apart from reactive power compensation; it has multiple roles in the operation of power systems. The auxiliary control signals to SVC play a very important role in mitigating the rotor electro-mechanical low frequency oscillations. Artificial neural network based controller is designed using the generator speed deviation, as a modulated signal to SVC, to generate the desired damping, is proposed in this paper. The ANN is trained using conventional controlled data and hence replaces the conventional controller. The ANN controlled SVC is used to improve the dynamic performance of power system by reducing the steady-state error and for its fast settling. The simulations are carried out for multi-machine power system (MMPS) at different operating conditions

Magnetocaloric effect in Gd/W thin film heterostructures

In an effort to understand the impact of nanostructuring on the magnetocaloric effect, we have grown and studied gadolinium in MgO/W(50 $\textrm{\AA}$)/[Gd(400 $\textrm{\AA}$)/W(50 $\textrm{\AA}$)]$_8$ heterostructures. The entropy change associated with the second order magnetic phase transition was determined from the isothermal magnetization for numerous temperatures and the appropriate Maxwell relation. The entropy change peaks at a temperature of 284 K with a value of approximately 3.4 J/kg-K for a 0-30 kOe field change; the full width at half max of the entropy change peak is about 70 K, which is significantly wider than that of bulk Gd under similar conditions. The relative cooling power of this nanoscale system is about 240 J/kg, somewhat lower than that of bulk Gd (410 J/kg). An iterative Kovel-Fisher method was used to determine the critical exponents governing the phase transition to be $\beta=0.51$, and $\gamma=1.75$. Along with a suppressed Curie temperature relative to the bulk, the fact that the convergent value of $\gamma$ is that predicted by the 2-D Ising model may suggest that finite size effects play an important role in this system. Together, these observations suggest that nanostructuring may be a promising route to tailoring the magnetocaloric response of materials

Speech Emotion Recognition System

Speech Emotion Recognition (SER) is a research topic which has a wide range of applications. The features of speech such as, Mel Frequency cepstrum coefficients (MFCC ) are extracted which are uttered in the speech. To classify different emotional states such as boredom, happiness, sadness, neutral, anger, from various emotional sound tracks from a database containing emotional speech SVM is used as classifier. SVM is used for classification of emotions. The accuracy obtained in SVM is very much higher. DOI: 10.17762/ijritcc2321-8169.15057