Optimizing the Dynamic Performance of a Wind Driven Standalone DFIG Using an Advanced Control Algorithm

The article seeks to improve the dynamic performance of a standalone doubly fed induction generator (DFIG) which driven by a wind turbine, with the help of an effective control approach. The superiority of the designed predictive controller can be confirmed through evaluating the performance of the DFIG under other control algorithm, which is the model predictive direct torque control (MPDTC), model predictive current control (MPCC) as classic types of control. Firstly, the operating principles of the two controllers are described in details. After that, a comprehensive comparison is performed among the dynamic performances of the designed MPDTC, MPCC techniques and the predictive control strategy, so we can easily present the merits and deficiencies of each control scheme to be able to easily select the most appropriate algorithm to be utilized with the DFIG. The comparison is carried out in terms of system simplicity, dynamic response, ripples’ content, number of performed commutations and total harmonic distortion (THD). The results of the comparison prove the effectiveness and validation of our proposed predictive controller; as it achieves the system simplicity, its dynamic response is faster than that of MPDTC and MPCC, it presents a lower content of ripples compared to MPDTC and MPCC. Moreover, it can minimize the computational burden, remarkably. Furthermore, the numerical results are showing a marked reduction in the THD with a percentage of 2.23 % compared to MPDTC and 1.8 % compared to MPCC. For these reasons, it can be said that the formulated controller is the most convenient to be used with the DFIG to achieve the best dynamic performance

Time-Fractional KdV Equation for the plasma in auroral zone using Variational Methods

The reductive perturbation method has been employed to derive the Korteweg-de Vries (KdV) equation for small but finite amplitude electrostatic waves. The Lagrangian of the time fractional KdV equation is used in similar form to the Lagrangian of the regular KdV equation. The variation of the functional of this Lagrangian leads to the Euler-Lagrange equation that leads to the time fractional KdV equation. The Riemann-Liouvulle definition of the fractional derivative is used to describe the time fractional operator in the fractional KdV equation. The variational-iteration method given by He is used to solve the derived time fractional KdV equation. The calculations of the solution with initial condition A0*sech(cx)^2 are carried out. Numerical studies have been made using plasma parameters close to those values corresponding to the dayside auroral zone. The effects of the time fractional parameter on the electrostatic solitary structures are presented.Comment: 1 tex file + 5 eps figure

Synthesis and Characterization of Five, Sevene Heterocyclic Membered Rings

New compounds containing heterocyclic units have been synthesized. These compounds include 2-amino 5- phenyl-1,3,4-thiadiazole (1) as starting material to prepare the Schiff bases 2N[3-nitrobenzylidene -2 hydroxy benzylidene and 4-N,N-dimethyl aminobenzylidene] -5-phenyl-1,3,4-thiadiazole (2abc) , 2N[3-nitrophenyl, 2-hydroxyphenyl or 4-N,N-dimethylaminophenyl] 3-]2-amino-5-phenyl-1,3,4-thiadiazole]-2,3-dihydro-[1,3]oxazepine-benzo-4,7-dione] (3abc), 2N[3-nitrophenyl,2-hydroxyphenyl,4-N,N-dimethylaminophenyl]-3-[2-amino-5-phenyl-1,3,4-thiadiazole-2-yl]-2,3-dihydro-[1,3]oxazepine-4,7-dione[(4abc), 2-N-[3-nitrophenyl, 2-hydroxyphenyl or 4-N,N-dimethylaminophenyl]-3-[2-amino-5-phenyl-1,3,4-thiadiazole-2yl]-1,2,3-trihydro-benzo-[1,2-e][1,3] diazepine-4,7-dione (5abc) ,2N[2-(3-nitrophenyl,2-hydroxyphenyl or 4-N,N-dimethylaminophenyl)]-4-oxo-1,3-thiazolidine-3-yl]-2-amino-5-phenyl-1,3,4-thiadiazole (6abc), 2-N-[5-(3-nitrophenyl,2-hydroxyphenyl or 4-N,N-dimethylaminophenyl)-tetrazolo-1-yl]-2-amino-5-phenyl-1,3,4-thiadiazole (7abc) , 2-N-[5-(3-nitrophenyl,2-hydroxyphenyl or 4-N,N-dimethylaminophenyl)-3-[2-amino-5-phenyl-1,3,4-thiadiazole-2-yl]-2,3-dihydro-[1,3]oxazepine-benzo-4,7-dithione (8abc) , 2-N-[5-(3-nitrophenyl,2-hydroxyphenyl or 4-N,N-dimethylaminophenyl)-3-[2-amino-5-phenyl-1,3,4-thiadiazole-2-yl]-2,3-dihydro-[1,3]oxazepine -4,7-dithione -5-ene (9abc) and 2-N-[5-(3-nitrophenyl,2-hydroxyphenyl or 4-N,N-dimethylaminophenyl)-3-[2-amino-5-phenyl-1,3,4-thiadiazole-2-yl] -1,2,3-trihydro-benzo-[1,2-e][1,3] diazepine -4,7-dithione - (10abc) . the structures of these compounds were characterized by FT-IR, 1H,13C-NMR,Uv/vis spectroscopy and the melting points were determined besides the evaluation of its biological activity

Bayesian Economists...Bayesian Agents II: Evolution of Beliefs in the Single Sector Growth Model

In "Bayesian Economists ... Bayesian Agents I" (BBI), we generalized the results on Bayesian learning based on the martingale convergence theorem from the repeated to the sequential framework. In BBI, we showed that the variability introduced by the sequential framework is sufficient under very mild identifiability conditions to circumvent the incomplete learning results that characterize the literature. In this paper, we demonstrate that result in the neo-classical single sector growth model under even weaker identifiability conditions. We study the evolution of agent-beliefs in that model and show that, under reasonable conditions, the dependence of the current capital stock on the previous capital stock induces enough variability for our complete learning results to become relevant. Not only does complete learning take place from the subjective point of view of the agents' priors, but it also takes place from the point of view of an objective observer (modeling economist) who knows the true structure

Bayesian Economist ... Bayesian Agents I: An Alternative Approach to Optimal Learning

We study the framework of optimal decision making under uncertainty where the agents do not know the full structure of the model and try to learn it optimally. We generalize the results on Bayesian learning based on the martingale convergence theorem to the sequential framework instead of the repeated framework for which results are currently available. We also show that the variability introduced by the sequential framework is sufficient under very mild identifiability conditions to circumvent the incomplete learning results that characterize the literature. We then question the type of convergence so achieved, and give an alternative Bayesian approach whereby we let the economist himself be a Bayesian with a prior on the priors that his agents may have. We prove that such an economist cannot justify endowing all his agents with the same (much less the true) prior on the basis that the model has been running long enough that we can almost surely approximate any agent's beliefs by any other's. We then examine a possibly weaker justification based on the convergence of the economist's measure on beliefs, and fully characterize it by the Harris ergodicity of the relevant Markov kernel. By means of very simple examples, we then show that learning, partial learning, and non-learning may all occur under the weak conditions that we impose. For complicated models where the Harris ergodicity of the Markov kernel in question can neither be proved nor disproved, the mathematical/statistical test of Domowitz and El-Gamal (1989) can be utilized