为了提高风电功率预测准确性，提出一种基于频率游程判别法和变分模态分解 （VMD）残差修正的风电功率超短期预测模型 . 采用变分模态分解将原始风电功率序列分解， 得到一系列不同中心频率的子序列，再利用序列之差提取残差序列，残差序列继承原始序列噪 声分量与分解被屏蔽的真实分量，呈现波动性大，非线性复杂和不平稳的特点，采用 t-SSALSTM模型并结合天气特征进行预测. 利用频率游程判别法把子序列划分为低频分量类和高频 分量类：低频分量呈现线性平稳的特点，采用自适应t分布麻雀搜索算法（t-SSA）优化自回归滑 动平均模型（ARIMA）预测；高频分量具有波动性大且复杂的特点，采用 t-SSA优化长短时记忆 神经网络（LSTM）进行预测. 将不同序列的预测结果线性叠加得到风电功率预测结果. 将该模 型应用于国内某风电发电厂的风电功率预测中，试验结果表明，该模型能有效提高预测精度.
To improve the accuracy of wind power prediction, an ultra-short-term combination forecasting method based on Frequency Run Length Discriminant and Variational Modal Decomposition (VMD) residual error correction is proposed. Firstly, the original wind power sequence is decomposed by VMD to obtain a series of subsequences with different center frequencies, and then the residual sequence is extracted from the difference in the se? quences. The residual sequence has the characteristics of large fluctuation, nonlinear complexity, and unsteadiness, which inherits the original sequence noise component and the masked information during decomposition, and the adaptive t-distribution Sparrow Search Algorithm (t-SSA-LSTM) combined with the weather features is used for the prediction. The sub-sequences are divided into two kinds of signals class, namely high and low- frequency se? quences, by using the Frequency Run Length Discriminant method. The low-frequency sequences are linear stability and the adaptive t-distribution Sparrow Search Algorithm (t-SSA) is used to optimize the autoregressive integrated moving average (ARIMA) model prediction. The characteristics of high-frequency sequences are volatile and com? plex, and the t-SSA is used to optimize the Long Short-Term Memory (LSTM) neural network for the prediction of high-frequency sequences. Finally, the wind power prediction results are achieved by linearly superimposing the pre? diction results of different sequences. The proposed model is finally applied to a wind farm in China, and the results show that the model can effectively improve the prediction accuracy.
王瑞 ,冉锋 ,逯静 .基于游程判别法和VMD残差修正的风电功率预测[J].湖南大学学报：自然科学版,2022,49(8):128~137复制