Analysis and Design of a Permanent-Magnet Outer-Rotor Synchronous Generator for a Direct-Drive Vertical-Axis Wind Turbine

In Permanent-Magnet Synchronous Generators (PMSGs) the reduction of cogging torque is one of the most important problems in their performance and evaluation. In this paper, at first, a direct-drive vertical-axis wind turbine is chosen. According to its nominal value operational point, necessary parameters for the generator is extracted. Due to an analytical method, four generators with different pole-slot combinations are designed. Average torque, torque ripple and cogging torque are evaluated based on finite element method. The combination with best performance is chosen and with the analysis of variation of effective parameters on cogging torque, and introducing a useful method, an improved design of the PMSG with lowest cogging torque and maximum average torque is obtained. The results show a proper performance and a correctness of the proposed method

Design of a Permanent-Magnet Synchronous Generator for a 2 MW Gearless Horizontal-Axis Wind Turbine According to its Capability Curves

Permanent-Magnet Synchronous Generators (PMSGs) exhibit high efficiency and power density, and have already been employed in gearless wind turbines. In the gearless wind turbines, due to the removal of the gearbox, the cogging torque is an important issue. Therefore, in this paper, at first, design of a Permanent-Magnet Synchronous Generator for a 2MW gearless horizontal-axis wind turbine, according to torque-speed and capability curves, is presented. For estimation of cogging torque in PMSGs, an analytical method is used. Performance and accuracy of this method is compared with the results of Finite Element Method (FEM). Considering the effect of dominant design parameters, cogging torque is efficiently reduced

An Efficient Framework of Utilizing the Latent Semantic Analysis in Text Extraction

The use of the latent semantic analysis (LSA) in text mining demands large space and time requirements. This paper proposes a new text extraction method that sets a framework on how to employ the statistical semantic analysis in the text extraction in an efficient way. The method uses the centrality feature and omits the segments of the text that have a high verbatim, statistical, or semantic similarity with previously processed segments. The identification of similarity is based on a new multi-layer similarity method that computes the similarity in three statistical layers, it uses the Jaccard similarity and the vector space model in the first and second layers respectively, and uses the LSA in the third layer. The multi-layer similarity restricts the use of the third layer for the segments that the first and second layers failed to estimate their similarities. Rouge tool is used in the evaluation, but because Rouge does not consider the extract’s size, we supplemented it with a new evaluation strategy based on the compression rate and the ratio of the sentences intersections between the automatic and the reference extracts. Our comparisons with classical LSA and traditional statistical extractions showed that we reduced the use of the LSA procedure by 52%, and we obtained 65% reduction on the original matrix dimensions, also, we obtained remarkable accuracy results. It is concluded that the employment of the centrality feature with the proposed multi-layer framework yields a significant solution in terms of efficiency and accuracy in the field of text extraction