In the modern world, wind turbine (WT) has become the largest source of renewable energy. The horizontal-axis wind turbine (HAWT) has higher efficiency than the vertical-axis wind turbine (VAWT). The blade pitch angle (BPA) of WT is controlled to increase output power generation over the rated wind speed. This paper proposes an accurate controller for BPA in a 500-kw HAWT. Three types of controllers have been applied and compared to find the best controller: PID controller (PIDC), fuzzy logic type-2 controller (T2FLC), and hybrid type-2 fuzzy-PID controller (T2FPIDC). This paper has been used Mamdani and Sugeno fuzzy inference systems (FIS) to find the best inference system for WT controllers. Furthermore, genetic algorithm (GA) and particle swarm optimization (PSO) have been applied and compared to find the best tuning of PID parameters. The results demonstrate that the hybrid T2FPIDC under the Sugeno FIS with optimal PID using PSO produces the best results in terms of stable output power at variable wind speeds and reducing the error in the output signal.
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
... Show Morewere prepared by condensation of 6-R-2amino bcnzothiazol with Salicyldehyde.These Schiff bases were found to reach with maleic anhydride and citraconic to give
A new Schiffbase derivative ligands [H4L1] and [H2L2] have been produced by condensed ophathaldehyde with ethylene diamine and [N1, N1'E, N1, N1'E)-N1, N1'-(1, 2-phenylenebis (methan-1-yl- 1ylidene)) diethane-1, 2-diamine] with 2-benzoyl benzoic acid. Schiffbase ligands have been separated and categorized by 1H, 13 C-NMR, (CHN) elemental analysis, UV-visible, mass spectroscopy and FTIR methods. Ten new coordination complexes were prepared and structurally diagnosed: [M(L1)Cl2] and [M2(L2)Cl2] where M(II) = Mn (II), Co(II), Ni(II), Cu(II) and Hg(II). The complexes have been typified by FTIR, UV-visble atomic absorption, molar conductance elemental analysis, and magnetic susceptibility. The details of the ligand (H4L1) compounds are getting a
... Show MoreThe reactions of ozone with 2,3-Dimethyl-2-Butene (CH3)2C=C(CH3)2 and 1,3-Butadiene CH2=CHCH=CH2 have been investigated under atmospheric conditions at 298±3K in air using both relative and absolute rate techniques, and the measured rate coefficients are found to be in good agreement in both techniques used. The obtained results show the addition of ozone to the double bond in these compounds and how it acts as function of the methyl group substituent situated on the double bond. The yields of all the main products have been determined using FTIR and GC-FID and the product studies of these reactions establish a very good idea for the decomposition pathways for the primary formed compounds (ozonides) and give a good information for the effe
... Show MoreIn this paper we estimate the coefficients and scale parameter in linear regression model depending on the residuals are of type 1 of extreme value distribution for the largest values . This can be regard as an improvement for the studies with the smallest values . We study two estimation methods ( OLS & MLE ) where we resort to Newton – Raphson (NR) and Fisher Scoring methods to get MLE estimate because the difficulty of using the usual approach with MLE . The relative efficiency criterion is considered beside to the statistical inference procedures for the extreme value regression model of type 1 for largest values . Confidence interval , hypothesis testing for both scale parameter and regression coefficients
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