A new distribution for modeling wind speed characteristics and evaluating wind power potential in Xinjiang, China

ABSTRACT

In this study, the authors conduct the research of the wind speed characteristics and the wind energy assessment based on the data at a height of 10 m in Xinjiang Uygur Autonomous Region which is located in the northwest part of China. The analysis shows that the wind is strong in the east-west direction, and the wind speed is concentrated in the range of 2 m/s to 9 m/s. Moreover, it has obvious periodicity and not large skewness, hence we need to find a model that can flexibly fit the wind speed distribution in the area. Then, we introduce a new flexible distribution for the first time, called Alpha logarithmic transformed Log-normal (ALTLN). Thirdly, the data are split and an analysis is made for the typically monthly, quarterly, and annual wind speed distribution and wind power density. The results show that the coefficient of determination (R²) is almost 99%, the root-mean-square error (RMSE) is less than 0.05, and most of the results pass the Kolmogorov–Smirnov (K-S) test which means the corresponding p values greater than 0.05 and the values are between 0.2 and 0.8. Finally, we come to the conclusions that the ALTLN distribution has good applicability to fit wind speed distribution and annual wind energy output reaches several thousands of gigawatt-hours, especially to Kashi. These results are helpful to develop the wind energy better for relevant departments.

KEYWORDS:

• Wind speed
• ALTLN distribution
• wind power
• Xinjiang Uygur autonomous region of China

Highlights

• The study solves a knowledge gap that existing wind speed distributions perform bad.

• Identification of a good model, called Alpha logarithmic transformed Log-normal.

• Evaluation of the new model quality through selected five kinds of evaluation criteria.

• Analysis variation of wind speed, model parameters, and wind power density.

• It provides an effective reference to assess and utilize this renewable energy.

Nomenclature

Table

Acknowledgments

The authors would like to thank the associate editor and two anonymous reviewers for their constructive comments, which helped improve the manuscript significantly.

Additional information

Funding

This research was financially supported by the National Natural Science Foundation of China [Grant No. 11861049], Natural Science Foundation of Inner Mongolia [Grant No. 2017MS0101]

Notes on contributors

Xiao-Yan An

Xiao-Yan An is currently pursuing the master degree at the Inner Mongolia University of Technology. Her main area of interest is statistical analysis of wind resource data.

Zaizai Yan

Zaizai Yan is a professor at the Inner Mongolia University of Technology. He received the Ph.D. degree in Mathematical Sciences from Xian Jiaotong University. He has been a doctoral supervisor since June 2007. He published more than 40 peer-reviewed articles in international journals and conferences. His research interests mainly focus on statistical methods for system reliability, mathematical methods of mechanical problems, survey sampling, and survival analysis.

Jun-Mei Jia

Jun-Mei Jia is an instructor at the Inner Mongolia University of Technology. She received the Ph.D. degree in Mechanics from Inner Mongolia University of Technology. Her research interest is statistical methods for system reliability.