Modeling of a Multi-Layers Artificial Neural Networks to estimate the Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of Photovoltaic Energy Systems

This research deals with the modeling of a Multi-Layers Feed Forward Artificial Neural Networks (MLFFNN), trained using Gradient Descent algorithm with Momentum factor & adaptive learning rate, to estimate the output of the neural network corresponding to the optimal Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of Photovoltaic Energy Systems. Thus, the DMPPT-ANN “Developed MPPT-ANN” controller proposed in this research, independent in his work on the use of electrical measurements output of PV system to determine the duty cycle, and without the need to use a Proportional-Integrative Controller to control the cycle of the work of the of DC-DC Boost Converter, and this improves the dynamic performance of the proposed controller to determine the optimal Duty Cycle accurately and quickly. In this context, this research discusses the optimal selection of the proposed MLFFNN structure in the research in terms of determining the optimum number of hidden layers and the optimal number of neurons in them, evaluating the values of the Mean square error and the resulting Correlation Coefficient after each training of the neural network. The final network model with the optimal structure is then adopted to form the DMPPT-ANN Controller to track the MPP point of the PV system. The simulation results performed in the Matlab / Simulink environment demonstrated the best performance of the proposed DMPPT-ANN controller based on the MLFFNN neural network model, by accurately estimating the Duty Cycle and improving the response speed of the PV system output to MPP access, , as well as finally eliminating the resulting oscillations in the steady state of the Power response curve of PV system compared with the use of a number of reference controls: an advanced tracking controller MPPT-ANN-PI based on ANN network to estimate MPP point voltage with conventional PI controller, a MPPT-FLC and a conventional MPPT-INC uses the Incremental Conductance technique INC

Numerical Analysis and Model tests of the Bearing Capacity of shallow Strip Footing resting on Multi-layered Soils

In present investigation attempt has been made to study the bearing capacity and settlement characteristics of footings subjected to central vertical load and resting on layered soil with the help of model tests and with the application of finite element method (FEM) to calculate bearing capacity of a strip footing on one-layer and two-layer soil (Sand and Clay). To investigate the effect of various parameters on soil bearing Capacity a commercial finite element software, PLAXIS, has been used. Soil profile contains two soil types including sand and clay. Soil behavior is represented by the elasto-plastic Mohrcoulomb (MC) -model. For a one-layer case, the bearing capacity also is calculated which has a good agreement with theoretical equations. For a layered soil, soft-over strong soil, parametric study was carried out. It is concluded that the bearing capacity of footing decreases as the height of clayey soil increases whilst the displacement under footing increases. There is a critical depth where the stronger bottom layer does not affect ultimate bearing capacity and failure mechanism of footing.

Assess Power System Voltage Stability Using Neural Networks

The contribution of our research include building an artificial neural network in MATLAB program environment and improvement of maximum loading point algorithm, to compute the most critical voltage stability margin, for on-line voltage stability assessment, and a method to approximate the most critical voltage stability margin accurately. a method to create a (ARTIFICIAL NEURAL NETWORK) approach.

Improving WLD for Facial Expressions recognition using Gabor filter

The aim of the work is to improve the performance of the WLD descriptor using Gabor filters in a preprocessing stage. The performance of the improved descriptor will be compared with the performance of the LBP descriptor(a widely used descriptor in FER researches). This performance will be achieved using the extremely used expert system SVM besides the expert systems CSD and MLP.