Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Investigation of electrically active defects at the interface of high-k dielectrics and compound semiconductors

As silicon based devices in integrated circuits reach the fundamental limits of dimensional scaling there is growing research interest in the use of high electron mobility channel materials, such as indium gallium arsenide (InGaAs), in conjunction with high dielectric constant (high-k) gate oxides, for Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) based devices. The motivation for employing high mobility channel materials is to reduce power dissipation in integrated circuits while also providing improved performance. One of the primary challenges to date in the field of III-V semiconductors has been the observation of high levels of defect densities at the high-k/III-V interface, which prevents surface inversion of the semiconductor. The work presented in this PhD thesis details the characterization of MOS devices incorporating high-k dielectrics on III-V semiconductors. The analysis examines the effect of modifying the semiconductor bandgap in MOS structures incorporating InxGa1-xAs (x: 0, 0.15. 0.3, 0.53) layers, the optimization of device passivation procedures designed to reduce interface defect densities, and analysis of such electrically active interface defect states for the high-k/InGaAs system. Devices are characterized primarily through capacitance-voltage (CV) and conductance-voltage (GV) measurements of MOS structures both as a function of frequency and temperature. In particular, the density of electrically active interface states was reduced to the level which allowed the observation of true surface inversion behavior in the In0.53Ga0.47As MOS system. This was achieved by developing an optimized (NH4)2S passivation, minimized air exposure, and atomic layer deposition of an Al2O3 gate oxide. An extraction of activation energies allows discrimination of the mechanisms responsible for the inversion response. Finally a new approach is described to determine the minority carrier generation lifetime and the oxide capacitance in MOS structures. The method is demonstrated for an In0.53Ga0.47As system, but is generally applicable to any MOS structure exhibiting a minority carrier response in inversion.