Thermal Stability Investigation of Synthesized Epoxy-Polyurethane/Silica Nanocomposites

Silicon - In this research, epoxy polyurethane-nano silica nanocomposites have been synthesized using an in-situ method, for which SiO2 nanocomposites had been initially ready in N,...     

Evaluation of the maximum loadability point of power systems considering the effect of static load models

Power system stability is an important problem for power system operation. Determination of different stability margins can result in the optimum utilization of power system with minimum risk. Voltage stability is an important subset of power system stability. To correctly analyze the voltage stability of a power system, suitable dynamic models are usually required. However, static analysis tools can give us useful information about long term voltage stability. Especially, maximum loadability point (MLP) of a power system can be effectively estimated by modal analysis of load flow Jacobians. MLP is one of the important boundaries of voltage stability feasible region that loading beyond which is of little practical meaning. In this paper, MLP boundary of power system is analyzed by means of static analysis tools and its differences with the other boundaries of voltage stability, like saddle node bifurcation, are discussed. Effect of reactive power limits of generators and different static load models on the MLP border is also evaluated.     

Prediction of Generators' Participation Factors and Oscillation Types for Dominant Oscillatory Modes of Power System

Small-signal rotor angle stability, also known as small-signal stability, is an important issue for power system operation. This stability is associated with inter-area, local, control, and torsional oscillations. To cope with it, the type and main drivers of the oscillations should be determined. In this way, the system operators can plan more effective preventive and corrective actions, e.g., to transfer power generation from more vulnerable units to less vulnerable ones. However, determining oscillation modes and their participation factors by means of conventional methods (e.g., time-domain simulation and modal analysis) is a challenging and time-consuming task, which is not appropriate for on-line environments, such as dispatching centers of power systems. In this article, a new viewpoint for this problem is proposed through modeling it as a forecast process by which the participation factors of generators for dominant modes as well as the oscillation types are predicted. A new prediction strategy, composed of an information-theoretic feature selection, a probabilistic neural network, and a line search procedure, is also presented to implement the forecast process. The effectiveness of the proposed approach for small-signal stability evaluation is extensively illustrated on the IEEE New England test system.     

Power quality events classification and recognition using a novel support vector algorithm

This paper presents a method of power quality classification using support vector machines (SVMs). In SVM training, the kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these kernel types, kernel parameters and features should be used for the SVM training. In this paper to get optimal features for the classifier two stage of feature selection has been used. In first stage mutual information feature selection (MIFS) and in the second stage correlation feature selection (CFS) techniques are used for feature extraction from signals to build distinguished patterns for classifiers. MIFS can reduce the dimensionality of inputs, speed up the training of the network and get better performance and with CFS can get optimal features. In order to create training and testing vectors, different disturbance classes were simulated using parametric equations i.e., pure sinusoid, sag, swell, harmonic, outage, sag and harmonics and swell and harmonics. Finally, the investigation results of this novel approach are shown. The test results show that the classifier has an excellent performance on training speed, reliability and accuracy.