Analysis of power generation efficiency of large Indian wind farms using computational fluid dynamics and polynomial regression

The Deccan coastline in India has a great potential for wind power generation, due to balanced wind speed spread over a long period of time. Most of the large wind farms (employing large wind turbines) in India employ state-of-the art wind turbines and are meeting the energy demands put upon them. Other sites, however, still use old and inefficient turbines and are not able to exploit their full potential.

India’s energy demand is set to increase in the upcoming years. It is essential, therefore, to revamp old designs. This dataset is a part of an attempt that aims to estimate the farms’ productivity by simulating the GE 1.5XLE turbines in these areas.

Simulation experiments were done in ANSYS to record the simulation data. A polynomial regression model was trained on this data in order to predict the estimated power output for any value of the input wind velocity. The results were then compared with existing data about the actual power output of the wind farms. Results were documented.

References for existing data:

Windpower (Wind energy market intelligence). Lalpur wind farm report. https://www.thewindpower.net/scripts/fpdf181/windfarm.php?id=27004 [Last checked February 16, 2019]

Windpower (Wind energy market intelligence). Indian wind farms data. [Last checked February 16, 2019] https://www.thewindpower.net/windfarms_list_en.php

References for wind velocity data:

a. Sandeep Chinta, Arun Agarwal, C. Raghavendra Rao. Wind Speed Model for Anantapur District, Western Andhra Pradesh, India. International Journal of Innovative Research in Advanced Engineering (IJIRAE) [2014]

b. World Weather Online, Monthly Climate Averages https://www.worldweatheronline.com/lang/en-in/chittoor-weather-averages/andhra-pradesh/in.aspx [Last checked February 16, 2019]

c. Indiastat; Indiastat Month Wise Mean Wind Speed https://www.indiastat.com/meteorological-data/22/weather-data/30130/month-wise-mean-wind-speed/30790/stats.aspx [Last checked February 16, 2019]

d. Meteoblue Weather history: [Last checked February 16, 2019] https://www.meteoblue.com/en/weather/archive/export/india_el-salvador_3585481?daterange=2019-01-09+to+2019-01-16&params=&params%5B%5D=32%3B10+m+above+gnd%3B31%3B10+m+above+gnd&params%5B%5D=32%3B80+m+above+gnd%3B31%3B80+m+above+gnd&params%5B%5D=180%3Bsfc&utc_offset=-6&aggregation=hourly&temperatureunit=CELSIUS&windspeedunit=METER_PER_SECOND

印度德干海岸线凭借其长时间内风速分布均衡的特性，具有巨大的风力发电潜力。印度大部分大型风力发电场（采用大型风力涡轮机）均采用最先进的涡轮机技术，以满足其能源需求。然而，其他一些地点仍使用老旧且效率低下的涡轮机，未能充分发挥其潜能。随着印度未来几年能源需求的不断增长，对老旧设计的改造显得尤为迫切。本数据集作为评估这些地区风力发电场生产力的尝试之一，旨在通过模拟通用电气1.5XLE涡轮机来估算其发电效率。模拟实验在ANSYS软件中完成，以记录相关模拟数据。基于这些数据，构建了多项式回归模型，以预测任意输入风速下的预计功率输出。随后，将实验结果与现有风力发电场实际功率输出的数据进行了对比，并予以记录。

现有数据的参考文献：

* 风能（风能市场情报）。Lalpur风力发电场报告。https://www.thewindpower.net/scripts/fpdf181/windfarm.php?id=27004 [最后检查日期：2019年2月16日]
* 风能（风能市场情报）。印度风力发电场数据。[最后检查日期：2019年2月16日] https://www.thewindpower.net/windfarms_list_en.php

风速数据的参考文献：

a. Sandeep Chinta, Arun Agarwal, C. Raghavendra Rao. 安塔普尔地区（安得拉邦西部）风速模型。国际创新工程研究杂志（IJIRAE）[2014]
b. World Weather Online，月平均气候数据。https://www.worldweatheronline.com/lang/en-in/chittoor-weather-averages/andhra-pradesh/in.aspx [最后检查日期：2019年2月16日]
c. Indiastat；Indiastat按月平均风速数据。https://www.indiastat.com/meteorological-data/22/weather-data/30130/month-wise-mean-wind-speed/30790/stats.aspx [最后检查日期：2019年2月16日]
d. Meteoblue天气历史数据。[最后检查日期：2019年2月16日] https://www.meteoblue.com/en/weather/archive/export/india_el-salvador_3585481?daterange=2019-01-09+to+2019-01-16&params=&params%5B%5D=32%3B10+m+above+gnd%3B31%3B10+m+above+gnd&params%5B%5D=32%3B80+m+above+gnd%3B31%3B80+m+above+gnd&params%5B%5D=180%3Bsfc&utc_offset=-6&aggregation=hourly&temperatureunit=CELSIUS&windspeedunit=METER_PER_SECOND