Analysis and Application of Wind Speed Interpolation in Wind Farm Based on BP Neural Network Method

Abstract

[Introduction] Accurate wind resource data is of great significance to wind resource evaluation and power generation calculation of wind farm. Due to mechanical failure, weather factors and human influence, there are many problems in wind speed data acquisition, such as short collection time, many discontinuities, data distortion and so on, which bring a lot of trouble to the evaluation of wind resources. [Method] At present, the traditional interpolation method based on MCP method for discontinuous data interpolation and fitting in the wind power industry is not accurate enough. In this paper, the wind resource data prediction scheme based on neural network algorithm was proposed for wind speed interpolation of wind turbine and wind speed interpolation of wind measurement mast. [Result] The results show that the interpolation effect of BP neural network is better than the traditional interpolation method, and the wind speed interpolation of anemometer tower in flat terrain is better than that in complex terrain. [Conclusion] The research shows that the wind speed interpolation technology based on BP neural network method is suitable for wind speed interpolation application of wind farm, and the accuracy of wind resource assessment is significantly improved.

Keywords

• bp neural network
• wind resource assessment
• wind speed interpolation
• wind speed prediction