Finite size effects on the electrical properties of sol–gel synthesized CoFe2O4 powders: deviation from Maxwell–Wagner theory and evidence of surface polarization effects

Fine particles of cobalt ferrite were synthesized by the sol–gel method. Subsequent heat treatment at different temperatures yielded cobalt ferrites having different grain sizes. X-ray diffraction studies were carried out to elucidate the structure of all the samples. Dielectric permittivity and ac conductivity of all the samples were evaluated as a function of frequency, temperature and grain size. The variation of permittivity and ac conductivity with frequency reveals that the dispersion is due to Maxwell–Wagner type interfacial polarization in general, with a noted variation from the expected behaviour for the cold synthesized samples. High permittivity and conductivity for small grains were explained on the basis of the correlated barrier-hopping model

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