基于自适应神经模糊推理系统的风电功率预测方法

对风电场输出功率进行精确的预测是保证含大规模风电电力系统安全稳定运行的重要手段。文中对多步滚动预测模式进行了分析,并建立了ANFIS(自适应神经模糊推理系统)预测模型,进而实现对风电功率的实时滚动预测。以吉林省西部某风电场的实测数据为例进行算例分析,其中在形成初始模糊推理系统结构时,采用的算法是减法聚类,该算法有效的避免了人工设定结构法产生的组合爆炸问题。将基于线性回归法、滑动平均法和持续法进行风电功率实时多步滚动预测时得到的预测结果与利用所提出的ANFIS预测方法得到的结果进行比较,结果表明后者的预测精度更高,进一步说明了ANFIS预测模型的有效性。

Wind Power Prediction Based on Adaptive Neuro-fuzzy Inference System

Accurate wind power prediction is an important method to guarantee the power system containing large-scale wind power to be safe and stable. This paper analyses the multi-step rolling prediction mode, establishes the ANFIS (adaptive neuro-fuzzy inference system) prediction model, and realizes real-time multi-step rolling prediction of wind power. Taking the real-measured data from a wind farm in the west of Jilin province as an example, case study is done. The subtraction clustering algorithm is used when it form initial fuzzy inference system structure. This algorithm is effective to avoid the combination explosion problem of the artificial setting structure method. The real-time multi-step rolling results of wind power based on the linear regression method, the moving average method and the persistence method are compared with the prediction results based on the proposed ANFIS prediction method. The result of case study shows that the prediction accuracy of the latter method is the highest, and it further illustrates the validity of the ANFIS model.