A large volume wind data for renewable energy applications

The objective of the collection of dataset is to calculate the wind energy potential in the selected location using large volume of wind dataset. The wind energy potential data were collected at 100 m height from MSL (Mean Sea Level) from 2014 to 2016. The wind speed and direction were used to analyse wind energy characteristics and suitable site for wind turbine installation. The maximum wind power density was observed at monitoring sites S1, S2, S3 and S4. The altitude of the monitoring station and geomorphology of the site significantly controls the wind power density.


Data
The wind data monitoring station was selected from National Institute of Wind energy (NIWE) web portal for long term monitoring of wind data such as wind speed, wind direction, temperature and pressure (Fig. 1). The raw data can be downloaded as only text file. Those text file data is converted into excel file and the required data set such as wind speed, wind direction is separated as given as supplementary material with this article for 12 locations from S1 to S12 as Dataset1.xlsxeDataset12.xlsx. Each Dataset file consist of data of a particular location for 3 years (2014, 2015 and 2016) for every 10 minutes. These data set in excel file consist of wind speed measured from two instrument one placed at north direction and one at south direction. For wind direction accuracy, the average of these two values are taken into account for the calculation of Wind Power Density (WPD). Fig. 2 represents the annual wind direction and speed of each location with wind frequency distribution. Table 1 represents the average wind direction of the each location, standard deviation (SD), wind power density and uncertainty of the data set for the period of 2014e2016. Table 2 shows the wind power density class for 100 m altitude data.

Experimental design, materials, and methods
The wind speed was monitored using field cup anemometer (Instrument Make -Adolf Thies GmbH&Co. KG, Germany). The wind direction was recorded using Thies compact TMR wind vane (KINTECH Engineering). The power density was calculated from wind direction and speed of the each monitoring location. The monitoring instrumental setup was installed at 100 m altitude in 12 monitoring location for continuous data collection for every 10 minutes. The large volume of wind data was processed using Microsoft Excel 2007 software package. The meteorological data processing, wind rose diagram and statistical analysis were carried out using MATLAB 2016 software package. Wind power density was calculated using meteorological parameters such as wind speed distribution, air density Specifications Table   Subject Renewable Energy, Sustainability and the Environment Specific subject area Application of Meteorological data in power production Type of data

Value of the Data
The large volume of wind direction data can be used to analyse, the average annual wind direction of the site. The wind data monitoring for large period was helpful to identify the suitable location for installing the wind turbines and also used to find the type of wind turbine. The wind data based power density of the each site can be used to find the geological and geomorphological control on wind power generation.  and cube of wind speed. The available wind potential (P a ) per unit area is perpendicular to the wind stream. According to Rehman et al., 1994 [1], the kinetic energy flux is expressed as follows: In the above equation, v is the wind speed (in m/s); r is the air density (in kg/m 3 ); and P a is the theoretically available wind potential (in w/m 2 ). The generation of wind power depend on the wind energy conversation system and intensity of the wind in particular location. According to the above concept, approximately, 40% of the available wind power must be reached at the maximum. According to Betz's [2] limit, the maximum extractable power P max from a system working at its optimum efficiency is limited by a power coefficient (0.593; [2]). This capacity factor makes the maximum extractable power approximately 59.3% of the theoretical wind power [3].
The monitoring sites were classified based on the geographical locations and the distance from the Western ghats pass (gaps). Among the twelve monitoring locations, five locations falls in Aralvaimozhi and Senkottah pass sector (L.No S1, S4, S6, S9 and S10). The remaining seven locations falling under Palghat pass sector (L.No S2, S3, S5, S7, S8, S11 and S12). The calculated average wind speed of monitoring locations (from 2014 to 2016) is ordered as: S4 > S1 > S3 >S6 > S11 > S2 > S12 > S9 > S8 > S5 > S10 > S7. The wind energy potential (W/m2) of the monitoring locations is proportional to the wind speed, which follows the above mentioned order. According to Poje and Cividini [4], the wind energy potential of the each sites were classified based on the wind power class. Among all the monitoring locations, station 1, 2, 3, 4 were falling between wind power class 5 to 7 ( Table 2). The other monitoring locations were falling between wind power class 1 to 3.  The outcome of the wind power class clearly reveals that the geomorphological features like altitude of the monitoring locations and Aralvaimozhi, Senkottah and Palaghat gaps significantly affects the wind power density of the individual sites. The above assumption was observed through the regular monitoring of the wind speed, direction and power density of the monitoring stations.

Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.