基于BP神经网络方法的风电场风速插补分析应用

  目的  准确的风资源数据对风场的风资源评估和发电量计算有着重大意义。由于机械故障、天气因素和人为影响等原因，风场内风速数据出现采集时间短、间断点多、数据失真等诸多问题，给风资源的评估带来不小的麻烦。  方法  现阶段风电行业内采用基于相关测量预测方法（MCP，Measure-Correlate Predict）（可称之为传统插补方法）进行间断数据的插补和拟合，准确性略显不足。文章针对风机风速插补和测风塔测试风速插补两种应用场景，提出基于BP神经网络算法的风资源数据预测插补方案，进行模型建立和预测。  结果  结果表明:BP神经网络插补效果优于传统插补方法，且平坦地形测风塔风速插补优于复杂地形风速插补。  结论  研究表明:基于BP神经网络方法的风电场风速插补技术适用于风电场风速插补应用，对风资源评估的准确性有明显提升。

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

  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.