Mathematical Analysis of Impact of Oil, Gold and Currency on Tehran Stock Exchange

International audienceThe use of economic factors has led to a lot of research on the behavior of stock markets. These economic factors can be evaluated simultaneously using statistical tools called joint. The present paper examines the impact of global oil, Currency (Dollar) and gold prices on the Tehran StockExchange, using conditional heteroscedastic Models. indeed, we used autoregressive conditional heteroscedastic (ARCH), the generalized ARCH (GARCH) to capture behavior of the volatility. Actual data of years obtained from the Iran Market is used to t the models

Improvement Transient Stability of Fixed Speed Wind Energy Conversion System by Using Transformer-Type Superconducting Fault Current Limiter

International audienceThe wind turbine generation system (WTGS) is one of the representative renewable energy systems. With the rapid development of WTGS and its increased capacity, the level of short circuit current will increase in distribution systems. The application of the Superconducting Fault Current Limiter (SFCL), would not only reduce the level of the short circuit current but also offer a reliable interconnection to the network. The transformer-type superconducting fault current limiter (SFCL) is one of the fault current limiters, and has many advantages such as design flexibility. In this paper, the effect of transformer-type SFCL on transient behavior of grid connected to WTGS is studied. The WTGS is considered as a fixed-speed system, equipped with a squirrel-cage induction generator. The drive-train is represented by two-mass model. The simulation results show that the transformer-type SFCL not only limits the fault current but also can improve the dynamic performance of the WTGS

Loss Minimization through the Allocation of DGs Considering the Stochastic Nature of Units

Smart grid as the cleaner alternative to the legacy power system can improve technical, economical, and environmental aspects of the system up to a considerable degree. In smart grids, Distributed Generation (DG) units; which play an important role, should be optimally allocated. In this paper, DG placement is conducted with the goal of loss minimization of the grid considering the technical limitations associated with the voltage profile of the buses as well as the stochastic nature of the DGs. In this paper, three different kinds of DGs are included which are wind turbines, solar panels, and biomass generators. The results related to the case study which is IEEE standard 33 bus system reveals that the costs can be dramatically decreased

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise