An analytical study of the power distribution pattterns of silica waveguides with high paraboloic cylindrical deformation

An attempt is made to determine the relative power distribution in a step-index parabolic cylindrical waveguide (PCW) with high deformation across the direction of propagation. The guide is assumed to be made of silica. The scalar field approximation is employed for the analysis under which a vanishing refractive-index (RI) difference in the waveguide materials is considered. Further, no approximation for folds- is used in the analytical treatment. Due to the geometry of such waceguides, PCWs lose the well-defined modal discreteness, and a kind of mode bunching is observed instead, which becomes much more prominent in PCWs with high bends. However, with the increase in cross-sectional size, the mode-bunching tendency is slightly reduced. The general expressions for power in the guiding and nonguiding sections are obtained, and the fractional power patterns in all of the sections are presented for PCWs of various cross-sectional dimensions. It is observed that the confinement of power in the core section is increased for PCWs of larger cross-sectional size. Moreover, a fairly uniform distribution of power is seen over the modes having intermediate values of propagation constants

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year