基于完全集成经验模态分解和模糊熵分频的短期风电功率预测  被引量:2

Short-term Wind Power Prediction Based on CEEMDAN and Fuzzy Entropy Frequency Division

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作  者:文博 陈芳芳[1] 胡道波 罗银榕 张倩倩 WEN Bo;CHEN Fang-fang;HU Dao-bo;LUO Yin-rong;ZHANG Qian-qian(School of Electrical and Information Engineering,Yunnan Minzu University,Kunming 650031,China)

机构地区:[1]云南民族大学电气信息工程学院,昆明650031

出  处:《科学技术与工程》2023年第25期10835-10845,共11页Science Technology and Engineering

摘  要:随着风电接入电力系统的比例日益增大,准确的风电功率预测显得更加重要。为此,提出了一种基于模糊熵和完全集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)的短期风电功率预测模型。采用完全集成经验模态分解将原始风电功率序列进行分解,得到一系列不同频率的子序列。再使用模糊熵(fuzzy entropy, FE)算法识别各频率分量特征,将子序列分量分为高、中频分量类和趋势项。趋势项为低频分量,具有较为平稳、波动性小的特点,采用麻雀算法(sparrow search algorithm, SSA)优化支持向量回归(support vector regression, SVR)进行预测;高、中频分量的波动性大且特点较为复杂,则采用SSA优化长短期记忆神经网络(long short-term memory, LSTM),同时引入注意力机制(attention mechanism, AM)对重要信息进行更好的权值分配。最后,经过实验结果分析表明,该模型具有更高的风电功率预测精度。With0x0E䥺Symbol`@@0x0F the increasing proportion of wind power connected to the power system,accurate wind power prediction becomes more and more important.Therefore,a short-term wind power prediction model based on fuzzy entropy and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)was proposed.The complete ensemble empirical mode decomposition was used to decompose the original wind power series to obtain a series of sub-series with different frequencies.The fuzzy entropy(FE)algorithm was then used to identify the characteristics of each frequency component,and the subsequence components were classified into high and medium frequency component classes and trend terms.The trend term was the low frequency component,which had the characteristics of smoothness and low volatility,and the sparrow search algorithm(SSA)was used to optimize the support vector regression(SVR)for prediction.The high and medium frequency components had high volatility and complex characteristics,and SSA was used to optimize long short-term memory(LSTM)and attention mechanism(AM)were introduced to better assign weights to important information.Finally,the analysis of experimental results show that the proposed method has higher accuracy in wind power prediction.

关 键 词:麻雀算法 LSTM模型 SVR模型 CEEMDAN分解 风电功率预测 模糊熵 

分 类 号:TM614[电气工程—电力系统及自动化]

 

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