In this paper, an analysis of basic airfoils profile for micro and small-scale horizontal axis wind turbine (HAWT) has been studied for different angle-of-attack and fixed Reynolds number. For turbine blade modeling, the data used is from National advisory committee of aeronautics (NACA) of five digit airfoil series. The comprehensive relationship between optimal angle-of-attack and Reynolds number has been analyzed. A low or high lift-to-drag ratio ( / ) can be identified at different angle-of-attack . The computational fluid dynamics analysis of NACA 63-415 airfoil is carried out at different angle-of-attack at wind speed 5 m/s using ANSYS/Fluent software. The pressure, turbulence and velocity distribution plots have been observed.Index Terms-Angle-of-attack ( ), coefficient of power ( ), horizontal axis wind turbine (HAWT), lift-to-drag ( / ) ratio, national advisory committee of aeronautics (NACA) series, reynolds number (Re) and tip speed ratio (TSR).

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In Natural Language Processing (NLP) pipelines, Named Entity Recognition (NER) is one of the preliminary problems, which marks proper nouns and other named entities such as Location, Person, Organization, Disease etc. Such entities, without an NER module, adversely affect the performance of a machine translation system. NER helps in overcoming this problem by recognising and handling such entities separately, although it can be useful in Information Extraction systems also. Bhojpuri, Maithili and Magahi are low resource languages, usually known as Purvanchal languages. This paper focuses on the development of an NER benchmark dataset for Machine Translation systems developed to translate from these languages to Hindi by annotating parts of the available corpora with named entities. Bhojpuri, Maithili and Magahi corpora of sizes 228373, 157468 and 56190 tokens, respectively, were annotated using 22 entity labels. The annotation considers coarse-grained annotation labels followed by the tagset used in one of the Hindi NER datasets. We also report a Deep Learning baseline that uses an LSTM-CNNs-CRF model. The lower baseline F 1 -scores from the NER tool obtained by using Conditional Random Fields models are 70.56% for Bhojpuri, 73.19% for Maithili and 84.18% for Magahi. The Deep Learning-based technique (LSTM-CNNs-CRF) achieved 61.41% for Bhojpuri, 71.38% for Maithili and 86.39% for Magahi. As the results show, LSTM-CNNs-CRF fails to outperform the lower baseline in the case of Bhojpuri and Maithili, which have more data in terms of the number of tokens, but not in terms of the number of named entities. However, the cross-lingual model training of LSTM-CNNs-CRF for Bhojpuri and Maithili performed better than the CRF.

Impact of traditional structured bridge-type fault current limiter (FCL), which is a auxiliary device and connected with doubly-fed induction generator (DFIG) based wind turbine system is observed in this present work. The traditional bridgetype FCL with discharging resistor is analyzed to improve the fault ride-through capability. A new control scheme has been presented in order to reduce the stator side fault current during a grid fault. The results of the simulation shows that the dynamic performance of the system is improved using the traditional bridge-type FCL. The DFIG wind turbine is associated with the grid and it is considered as an infinite bus. At the point of the interconnection between the wind energy conversion system and the grid, a symmetrical fault is taken. The results of traditional bridge-type FCL are compared with the series dynamic breaking resistor (SDBR) and without auxiliary controller. It is found that traditional bridge-type FCL works better than the SDBR. The entire system simulation is carried out using PSCAD/EMTDC software. Index Terms-Bridge-type fault current limiter (FCL), doublyfed induction generator (DFIG), fault ride-through (FRT) capability, series dynamic braking resistor (SDBR), wind turbine.