Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China

• This study uses Sliding T-test and Sliding F-test to do mutation tests.
• Tests results recommend to develop a perfect monitoring system for wind farms.
• Four probability density functions are used to evaluate wind speed and power.
• Three intelligent optimization algorithms estimate Weibull’s parameters.
• Three assessment indexes are used to choose the most fitting wind turbine.

The exploration of wind energy has become one of the most significant aims for countries all around the world. This is due to its low impact on the environment and its sustainable development. Therefore, it is very important to develop an effective and scientific way to evaluate wind resource potential and so that suitable wind turbines can be chosen. In this study, the 4-times daily wind speed data for the past 63 years in Huitengxile of Inner Mongolia in China was collected first to do mutation tests using Sliding T-test and Sliding F-test. The test results indicated that the wind speeds exhibited a significant change in the mean value and a big variation in variance. Secondly, in order to improve the assessment accuracy, three intelligent optimization algorithms were applied to estimate Weibull’s parameters, including Particle Swarm Optimization (PSO), Differential Evolution (DE) and Genetic Algorithm (GA). Finally, some new criteria, such as matching index, turbine cost index and the integrated matching index, were proposed in order to choose the most fitting wind turbine in accordance with the local environment and economic cost.