基于经验模态分解的PSO-SVM风电功率短期预测

针对风电功率预测对精确度的要求,结合风电机组功率特性曲线及支持向量机非线性拟合,提出了一种基于经验模态分解(EMD)的粒子群算法(PSO)优化支持向量机(SVM)功率短期预测模型。即将EMD分解后的各个风速序列分量通过PSO-SVM模型预测,将得到的各分量预测结果叠加后得到风速预测值,将该值输入功率转化曲线,即可得到最终的风电功率预测结果,以实现对风电机组功率的预测。通过对某地区风电场实际风速为例进行的仿真误差对比分析,得知该组合预测模型不仅有效可行,且有效提高了短期风电功率的预测精度。

Short-Term Prediction of PSO-SVM Wind Power Based on Empirical Mode Decomposition

In view of the accuracy requirement of wind power prediction, combined with the power characteristic curves of wind turbines and the nonlinear fitting of support vector machines, a particle swarm optimization (PSO) algorithm based on empirical mode decomposition (EMD) has thus been proposed to optimize the short-term prediction model of support vector machine (SVM) power. Based on the prediction of each wind speed sequence component after EMD decomposition by PSO-SVM model, the predicted values of each component will be superimposed, thus obtaining the predicted value of the wind speed. With the value input into the power conversion curve, the result of the final wind power prediction can be worked out, and the prediction of the power of wind turbines can be realized. A comparative analysis of the simulation error of the actual wind speed in a certain area verifies the effectiveness and feasibility of the combined forecasting mode, which effectively improves the prediction accuracy of the short-term wind power.